add the patch for torch 2.1.0 and 2.1.1

patch/eigen/common.h

+// Copyright (c) 2023 The pybind Community.
+
+#pragma once
+
+// Common message for `static_assert()`s, which are useful to easily
+// preempt much less obvious errors.
+#define PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED                                    \
+    "Pointer types (in particular `PyObject *`) are not supported as scalar types for Eigen "     \
+    "types."

patch/eigen/matrix.h

+/*
+    pybind11/eigen/matrix.h: Transparent conversion for dense and sparse Eigen matrices
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "../numpy.h"
+#include "common.h"
+
+/* HINT: To suppress warnings originating from the Eigen headers, use -isystem.
+   See also:
+       https://stackoverflow.com/questions/2579576/i-dir-vs-isystem-dir
+       https://stackoverflow.com/questions/1741816/isystem-for-ms-visual-studio-c-compiler
+*/
+PYBIND11_WARNING_PUSH
+PYBIND11_WARNING_DISABLE_MSVC(5054) // https://github.com/pybind/pybind11/pull/3741
+//       C5054: operator '&': deprecated between enumerations of different types
+#if defined(__MINGW32__)
+PYBIND11_WARNING_DISABLE_GCC("-Wmaybe-uninitialized")
+#endif
+
+#include <Eigen/Core>
+#include <Eigen/SparseCore>
+
+PYBIND11_WARNING_POP
+
+// Eigen prior to 3.2.7 doesn't have proper move constructors--but worse, some classes get implicit
+// move constructors that break things.  We could detect this an explicitly copy, but an extra copy
+// of matrices seems highly undesirable.
+static_assert(EIGEN_VERSION_AT_LEAST(3, 2, 7),
+              "Eigen matrix support in pybind11 requires Eigen >= 3.2.7");
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+
+// Provide a convenience alias for easier pass-by-ref usage with fully dynamic strides:
+using EigenDStride = Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic>;
+template <typename MatrixType>
+using EigenDRef = Eigen::Ref<MatrixType, 0, EigenDStride>;
+template <typename MatrixType>
+using EigenDMap = Eigen::Map<MatrixType, 0, EigenDStride>;
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+#if EIGEN_VERSION_AT_LEAST(3, 3, 0)
+using EigenIndex = Eigen::Index;
+template <typename Scalar, int Flags, typename StorageIndex>
+using EigenMapSparseMatrix = Eigen::Map<Eigen::SparseMatrix<Scalar, Flags, StorageIndex>>;
+#else
+using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE;
+template <typename Scalar, int Flags, typename StorageIndex>
+using EigenMapSparseMatrix = Eigen::MappedSparseMatrix<Scalar, Flags, StorageIndex>;
+#endif
+
+// Matches Eigen::Map, Eigen::Ref, blocks, etc:
+template <typename T>
+using is_eigen_dense_map = all_of<is_template_base_of<Eigen::DenseBase, T>,
+                                  std::is_base_of<Eigen::MapBase<T, Eigen::ReadOnlyAccessors>, T>>;
+template <typename T>
+using is_eigen_mutable_map = std::is_base_of<Eigen::MapBase<T, Eigen::WriteAccessors>, T>;
+template <typename T>
+using is_eigen_dense_plain
+    = all_of<negation<is_eigen_dense_map<T>>, is_template_base_of<Eigen::PlainObjectBase, T>>;
+template <typename T>
+using is_eigen_sparse = is_template_base_of<Eigen::SparseMatrixBase, T>;
+// Test for objects inheriting from EigenBase<Derived> that aren't captured by the above.  This
+// basically covers anything that can be assigned to a dense matrix but that don't have a typical
+// matrix data layout that can be copied from their .data().  For example, DiagonalMatrix and
+// SelfAdjointView fall into this category.
+template <typename T>
+using is_eigen_other
+    = all_of<is_template_base_of<Eigen::EigenBase, T>,
+             negation<any_of<is_eigen_dense_map<T>, is_eigen_dense_plain<T>, is_eigen_sparse<T>>>>;
+
+// Captures numpy/eigen conformability status (returned by EigenProps::conformable()):
+template <bool EigenRowMajor>
+struct EigenConformable {
+    bool conformable = false;
+    EigenIndex rows = 0, cols = 0;
+    EigenDStride stride{0, 0};    // Only valid if negativestrides is false!
+    bool negativestrides = false; // If true, do not use stride!
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    EigenConformable(bool fits = false) : conformable{fits} {}
+    // Matrix type:
+    EigenConformable(EigenIndex r, EigenIndex c, EigenIndex rstride, EigenIndex cstride)
+        : conformable{true}, rows{r}, cols{c},
+          // TODO: when Eigen bug #747 is fixed, remove the tests for non-negativity.
+          // http://eigen.tuxfamily.org/bz/show_bug.cgi?id=747
+          stride{EigenRowMajor ? (rstride > 0 ? rstride : 0)
+                               : (cstride > 0 ? cstride : 0) /* outer stride */,
+                 EigenRowMajor ? (cstride > 0 ? cstride : 0)
+                               : (rstride > 0 ? rstride : 0) /* inner stride */},
+          negativestrides{rstride < 0 || cstride < 0} {}
+    // Vector type:
+    EigenConformable(EigenIndex r, EigenIndex c, EigenIndex stride)
+        : EigenConformable(r, c, r == 1 ? c * stride : stride, c == 1 ? r : r * stride) {}
+
+    template <typename props>
+    bool stride_compatible() const {
+        // To have compatible strides, we need (on both dimensions) one of fully dynamic strides,
+        // matching strides, or a dimension size of 1 (in which case the stride value is
+        // irrelevant). Alternatively, if any dimension size is 0, the strides are not relevant
+        // (and numpy ≥ 1.23 sets the strides to 0 in that case, so we need to check explicitly).
+        if (negativestrides) {
+            return false;
+        }
+        if (rows == 0 || cols == 0) {
+            return true;
+        }
+        return (props::inner_stride == Eigen::Dynamic || props::inner_stride == stride.inner()
+                || (EigenRowMajor ? cols : rows) == 1)
+               && (props::outer_stride == Eigen::Dynamic || props::outer_stride == stride.outer()
+                   || (EigenRowMajor ? rows : cols) == 1);
+    }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator bool() const { return conformable; }
+};
+
+template <typename Type>
+struct eigen_extract_stride {
+    using type = Type;
+};
+template <typename PlainObjectType, int MapOptions, typename StrideType>
+struct eigen_extract_stride<Eigen::Map<PlainObjectType, MapOptions, StrideType>> {
+    using type = StrideType;
+};
+template <typename PlainObjectType, int Options, typename StrideType>
+struct eigen_extract_stride<Eigen::Ref<PlainObjectType, Options, StrideType>> {
+    using type = StrideType;
+};
+
+// Helper struct for extracting information from an Eigen type
+template <typename Type_>
+struct EigenProps {
+    using Type = Type_;
+    using Scalar = typename Type::Scalar;
+    using StrideType = typename eigen_extract_stride<Type>::type;
+    static constexpr EigenIndex rows = Type::RowsAtCompileTime, cols = Type::ColsAtCompileTime,
+                                size = Type::SizeAtCompileTime;
+    static constexpr bool row_major = Type::IsRowMajor,
+                          vector
+                          = Type::IsVectorAtCompileTime, // At least one dimension has fixed size 1
+        fixed_rows = rows != Eigen::Dynamic, fixed_cols = cols != Eigen::Dynamic,
+                          fixed = size != Eigen::Dynamic, // Fully-fixed size
+        dynamic = !fixed_rows && !fixed_cols;             // Fully-dynamic size
+
+    template <EigenIndex i, EigenIndex ifzero>
+    using if_zero = std::integral_constant<EigenIndex, i == 0 ? ifzero : i>;
+    static constexpr EigenIndex inner_stride
+        = if_zero<StrideType::InnerStrideAtCompileTime, 1>::value,
+        outer_stride = if_zero < StrideType::OuterStrideAtCompileTime,
+        vector      ? size
+        : row_major ? cols
+                    : rows > ::value;
+    static constexpr bool dynamic_stride
+        = inner_stride == Eigen::Dynamic && outer_stride == Eigen::Dynamic;
+    static constexpr bool requires_row_major
+        = !dynamic_stride && !vector && (row_major ? inner_stride : outer_stride) == 1;
+    static constexpr bool requires_col_major
+        = !dynamic_stride && !vector && (row_major ? outer_stride : inner_stride) == 1;
+
+    // Takes an input array and determines whether we can make it fit into the Eigen type.  If
+    // the array is a vector, we attempt to fit it into either an Eigen 1xN or Nx1 vector
+    // (preferring the latter if it will fit in either, i.e. for a fully dynamic matrix type).
+    static EigenConformable<row_major> conformable(const array &a) {
+        const auto dims = a.ndim();
+        if (dims < 1 || dims > 2) {
+            return false;
+        }
+
+        if (dims == 2) { // Matrix type: require exact match (or dynamic)
+
+            EigenIndex np_rows = a.shape(0), np_cols = a.shape(1),
+                       np_rstride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar)),
+                       np_cstride = a.strides(1) / static_cast<ssize_t>(sizeof(Scalar));
+            if ((fixed_rows && np_rows != rows) || (fixed_cols && np_cols != cols)) {
+                return false;
+            }
+
+            return {np_rows, np_cols, np_rstride, np_cstride};
+        }
+
+        // Otherwise we're storing an n-vector.  Only one of the strides will be used, but
+        // whichever is used, we want the (single) numpy stride value.
+        const EigenIndex n = a.shape(0),
+                         stride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar));
+
+        if (vector) { // Eigen type is a compile-time vector
+            if (fixed && size != n) {
+                return false; // Vector size mismatch
+            }
+            return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride};
+        }
+        if (fixed) {
+            // The type has a fixed size, but is not a vector: abort
+            return false;
+        }
+        if (fixed_cols) {
+            // Since this isn't a vector, cols must be != 1.  We allow this only if it exactly
+            // equals the number of elements (rows is Dynamic, and so 1 row is allowed).
+            if (cols != n) {
+                return false;
+            }
+            return {1, n, stride};
+        } // Otherwise it's either fully dynamic, or column dynamic; both become a column vector
+        if (fixed_rows && rows != n) {
+            return false;
+        }
+        return {n, 1, stride};
+    }
+
+    static constexpr bool show_writeable
+        = is_eigen_dense_map<Type>::value && is_eigen_mutable_map<Type>::value;
+    static constexpr bool show_order = is_eigen_dense_map<Type>::value;
+    static constexpr bool show_c_contiguous = show_order && requires_row_major;
+    static constexpr bool show_f_contiguous
+        = !show_c_contiguous && show_order && requires_col_major;
+
+    static constexpr auto descriptor
+        = const_name("numpy.ndarray[") + npy_format_descriptor<Scalar>::name + const_name("[")
+          + const_name<fixed_rows>(const_name<(size_t) rows>(), const_name("m")) + const_name(", ")
+          + const_name<fixed_cols>(const_name<(size_t) cols>(), const_name("n")) + const_name("]")
+          +
+          // For a reference type (e.g. Ref<MatrixXd>) we have other constraints that might need to
+          // be satisfied: writeable=True (for a mutable reference), and, depending on the map's
+          // stride options, possibly f_contiguous or c_contiguous.  We include them in the
+          // descriptor output to provide some hint as to why a TypeError is occurring (otherwise
+          // it can be confusing to see that a function accepts a 'numpy.ndarray[float64[3,2]]' and
+          // an error message that you *gave* a numpy.ndarray of the right type and dimensions.
+          const_name<show_writeable>(", flags.writeable", "")
+          + const_name<show_c_contiguous>(", flags.c_contiguous", "")
+          + const_name<show_f_contiguous>(", flags.f_contiguous", "") + const_name("]");
+};
+
+// Casts an Eigen type to numpy array.  If given a base, the numpy array references the src data,
+// otherwise it'll make a copy.  writeable lets you turn off the writeable flag for the array.
+template <typename props>
+handle
+eigen_array_cast(typename props::Type const &src, handle base = handle(), bool writeable = true) {
+    constexpr ssize_t elem_size = sizeof(typename props::Scalar);
+    array a;
+    if (props::vector) {
+        a = array({src.size()}, {elem_size * src.innerStride()}, src.data(), base);
+    } else {
+        a = array({src.rows(), src.cols()},
+                  {elem_size * src.rowStride(), elem_size * src.colStride()},
+                  src.data(),
+                  base);
+    }
+
+    if (!writeable) {
+        array_proxy(a.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
+    }
+
+    return a.release();
+}
+
+// Takes an lvalue ref to some Eigen type and a (python) base object, creating a numpy array that
+// reference the Eigen object's data with `base` as the python-registered base class (if omitted,
+// the base will be set to None, and lifetime management is up to the caller).  The numpy array is
+// non-writeable if the given type is const.
+template <typename props, typename Type>
+handle eigen_ref_array(Type &src, handle parent = none()) {
+    // none here is to get past array's should-we-copy detection, which currently always
+    // copies when there is no base.  Setting the base to None should be harmless.
+    return eigen_array_cast<props>(src, parent, !std::is_const<Type>::value);
+}
+
+// Takes a pointer to some dense, plain Eigen type, builds a capsule around it, then returns a
+// numpy array that references the encapsulated data with a python-side reference to the capsule to
+// tie its destruction to that of any dependent python objects.  Const-ness is determined by
+// whether or not the Type of the pointer given is const.
+template <typename props, typename Type, typename = enable_if_t<is_eigen_dense_plain<Type>::value>>
+handle eigen_encapsulate(Type *src) {
+    capsule base(src, [](void *o) { delete static_cast<Type *>(o); });
+    return eigen_ref_array<props>(*src, base);
+}
+
+// Type caster for regular, dense matrix types (e.g. MatrixXd), but not maps/refs/etc. of dense
+// types.
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_dense_plain<Type>::value>> {
+    using Scalar = typename Type::Scalar;
+    static_assert(!std::is_pointer<Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using props = EigenProps<Type>;
+
+    bool load(handle src, bool convert) {
+        // If we're in no-convert mode, only load if given an array of the correct type
+        if (!convert && !isinstance<array_t<Scalar>>(src)) {
+            return false;
+        }
+
+        // Coerce into an array, but don't do type conversion yet; the copy below handles it.
+        auto buf = array::ensure(src);
+
+        if (!buf) {
+            return false;
+        }
+
+        auto dims = buf.ndim();
+        if (dims < 1 || dims > 2) {
+            return false;
+        }
+
+        auto fits = props::conformable(buf);
+        if (!fits) {
+            return false;
+        }
+
+        // Allocate the new type, then build a numpy reference into it
+        value = Type(fits.rows, fits.cols);
+        auto ref = reinterpret_steal<array>(eigen_ref_array<props>(value));
+        if (dims == 1) {
+            ref = ref.squeeze();
+        } else if (ref.ndim() == 1) {
+            buf = buf.squeeze();
+        }
+
+        int result = detail::npy_api::get().PyArray_CopyInto_(ref.ptr(), buf.ptr());
+
+        if (result < 0) { // Copy failed!
+            PyErr_Clear();
+            return false;
+        }
+
+        return true;
+    }
+
+private:
+    // Cast implementation
+    template <typename CType>
+    static handle cast_impl(CType *src, return_value_policy policy, handle parent) {
+        switch (policy) {
+            case return_value_policy::take_ownership:
+            case return_value_policy::automatic:
+                return eigen_encapsulate<props>(src);
+            case return_value_policy::move:
+                return eigen_encapsulate<props>(new CType(std::move(*src)));
+            case return_value_policy::copy:
+                return eigen_array_cast<props>(*src);
+            case return_value_policy::reference:
+            case return_value_policy::automatic_reference:
+                return eigen_ref_array<props>(*src);
+            case return_value_policy::reference_internal:
+                return eigen_ref_array<props>(*src, parent);
+            default:
+                throw cast_error("unhandled return_value_policy: should not happen!");
+        };
+    }
+
+public:
+    // Normal returned non-reference, non-const value:
+    static handle cast(Type &&src, return_value_policy /* policy */, handle parent) {
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+    // If you return a non-reference const, we mark the numpy array readonly:
+    static handle cast(const Type &&src, return_value_policy /* policy */, handle parent) {
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+    // lvalue reference return; default (automatic) becomes copy
+    static handle cast(Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast_impl(&src, policy, parent);
+    }
+    // const lvalue reference return; default (automatic) becomes copy
+    static handle cast(const Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(&src, policy, parent);
+    }
+    // non-const pointer return
+    static handle cast(Type *src, return_value_policy policy, handle parent) {
+        return cast_impl(src, policy, parent);
+    }
+    // const pointer return
+    static handle cast(const Type *src, return_value_policy policy, handle parent) {
+        return cast_impl(src, policy, parent);
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type *() { return &value; }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &() { return value; }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &&() && { return std::move(value); }
+    template <typename T>
+    using cast_op_type = movable_cast_op_type<T>;
+
+private:
+    Type value;
+};
+
+// Base class for casting reference/map/block/etc. objects back to python.
+template <typename MapType>
+struct eigen_map_caster {
+    static_assert(!std::is_pointer<typename MapType::Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+
+private:
+    using props = EigenProps<MapType>;
+
+public:
+    // Directly referencing a ref/map's data is a bit dangerous (whatever the map/ref points to has
+    // to stay around), but we'll allow it under the assumption that you know what you're doing
+    // (and have an appropriate keep_alive in place).  We return a numpy array pointing directly at
+    // the ref's data (The numpy array ends up read-only if the ref was to a const matrix type.)
+    // Note that this means you need to ensure you don't destroy the object in some other way (e.g.
+    // with an appropriate keep_alive, or with a reference to a statically allocated matrix).
+    static handle cast(const MapType &src, return_value_policy policy, handle parent) {
+        switch (policy) {
+            case return_value_policy::copy:
+                return eigen_array_cast<props>(src);
+            case return_value_policy::reference_internal:
+                return eigen_array_cast<props>(src, parent, is_eigen_mutable_map<MapType>::value);
+            case return_value_policy::reference:
+            case return_value_policy::automatic:
+            case return_value_policy::automatic_reference:
+                return eigen_array_cast<props>(src, none(), is_eigen_mutable_map<MapType>::value);
+            default:
+                // move, take_ownership don't make any sense for a ref/map:
+                pybind11_fail("Invalid return_value_policy for Eigen Map/Ref/Block type");
+        }
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
+    // types but not bound arguments).  We still provide them (with an explicitly delete) so that
+    // you end up here if you try anyway.
+    bool load(handle, bool) = delete;
+    operator MapType() = delete;
+    template <typename>
+    using cast_op_type = MapType;
+};
+
+// We can return any map-like object (but can only load Refs, specialized next):
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_dense_map<Type>::value>> : eigen_map_caster<Type> {};
+
+// Loader for Ref<...> arguments.  See the documentation for info on how to make this work without
+// copying (it requires some extra effort in many cases).
+template <typename PlainObjectType, typename StrideType>
+struct type_caster<
+    Eigen::Ref<PlainObjectType, 0, StrideType>,
+    enable_if_t<is_eigen_dense_map<Eigen::Ref<PlainObjectType, 0, StrideType>>::value>>
+    : public eigen_map_caster<Eigen::Ref<PlainObjectType, 0, StrideType>> {
+private:
+    using Type = Eigen::Ref<PlainObjectType, 0, StrideType>;
+    using props = EigenProps<Type>;
+    using Scalar = typename props::Scalar;
+    static_assert(!std::is_pointer<Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using MapType = Eigen::Map<PlainObjectType, 0, StrideType>;
+    using Array
+        = array_t<Scalar,
+                  array::forcecast
+                      | ((props::row_major ? props::inner_stride : props::outer_stride) == 1
+                             ? array::c_style
+                         : (props::row_major ? props::outer_stride : props::inner_stride) == 1
+                             ? array::f_style
+                             : 0)>;
+    static constexpr bool need_writeable = is_eigen_mutable_map<Type>::value;
+    // Delay construction (these have no default constructor)
+    std::unique_ptr<MapType> map;
+    std::unique_ptr<Type> ref;
+    // Our array.  When possible, this is just a numpy array pointing to the source data, but
+    // sometimes we can't avoid copying (e.g. input is not a numpy array at all, has an
+    // incompatible layout, or is an array of a type that needs to be converted).  Using a numpy
+    // temporary (rather than an Eigen temporary) saves an extra copy when we need both type
+    // conversion and storage order conversion.  (Note that we refuse to use this temporary copy
+    // when loading an argument for a Ref<M> with M non-const, i.e. a read-write reference).
+    Array copy_or_ref;
+
+public:
+    bool load(handle src, bool convert) {
+        // First check whether what we have is already an array of the right type.  If not, we
+        // can't avoid a copy (because the copy is also going to do type conversion).
+        bool need_copy = !isinstance<Array>(src);
+
+        EigenConformable<props::row_major> fits;
+        if (!need_copy) {
+            // We don't need a converting copy, but we also need to check whether the strides are
+            // compatible with the Ref's stride requirements
+            auto aref = reinterpret_borrow<Array>(src);
+
+            if (aref && (!need_writeable || aref.writeable())) {
+                fits = props::conformable(aref);
+                if (!fits) {
+                    return false; // Incompatible dimensions
+                }
+                if (!fits.template stride_compatible<props>()) {
+                    need_copy = true;
+                } else {
+                    copy_or_ref = std::move(aref);
+                }
+            } else {
+                need_copy = true;
+            }
+        }
+
+        if (need_copy) {
+            // We need to copy: If we need a mutable reference, or we're not supposed to convert
+            // (either because we're in the no-convert overload pass, or because we're explicitly
+            // instructed not to copy (via `py::arg().noconvert()`) we have to fail loading.
+            if (!convert || need_writeable) {
+                return false;
+            }
+
+            Array copy = Array::ensure(src);
+            if (!copy) {
+                return false;
+            }
+            fits = props::conformable(copy);
+            if (!fits || !fits.template stride_compatible<props>()) {
+                return false;
+            }
+            copy_or_ref = std::move(copy);
+            loader_life_support::add_patient(copy_or_ref);
+        }
+
+        ref.reset();
+        map.reset(new MapType(data(copy_or_ref),
+                              fits.rows,
+                              fits.cols,
+                              make_stride(fits.stride.outer(), fits.stride.inner())));
+        ref.reset(new Type(*map));
+
+        return true;
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type *() { return ref.get(); }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &() { return *ref; }
+    template <typename _T>
+    using cast_op_type = pybind11::detail::cast_op_type<_T>;
+
+private:
+    template <typename T = Type, enable_if_t<is_eigen_mutable_map<T>::value, int> = 0>
+    Scalar *data(Array &a) {
+        return a.mutable_data();
+    }
+
+    template <typename T = Type, enable_if_t<!is_eigen_mutable_map<T>::value, int> = 0>
+    const Scalar *data(Array &a) {
+        return a.data();
+    }
+
+    // Attempt to figure out a constructor of `Stride` that will work.
+    // If both strides are fixed, use a default constructor:
+    template <typename S>
+    using stride_ctor_default = bool_constant<S::InnerStrideAtCompileTime != Eigen::Dynamic
+                                              && S::OuterStrideAtCompileTime != Eigen::Dynamic
+                                              && std::is_default_constructible<S>::value>;
+    // Otherwise, if there is a two-index constructor, assume it is (outer,inner) like
+    // Eigen::Stride, and use it:
+    template <typename S>
+    using stride_ctor_dual
+        = bool_constant<!stride_ctor_default<S>::value
+                        && std::is_constructible<S, EigenIndex, EigenIndex>::value>;
+    // Otherwise, if there is a one-index constructor, and just one of the strides is dynamic, use
+    // it (passing whichever stride is dynamic).
+    template <typename S>
+    using stride_ctor_outer
+        = bool_constant<!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value
+                        && S::OuterStrideAtCompileTime == Eigen::Dynamic
+                        && S::InnerStrideAtCompileTime != Eigen::Dynamic
+                        && std::is_constructible<S, EigenIndex>::value>;
+    template <typename S>
+    using stride_ctor_inner
+        = bool_constant<!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value
+                        && S::InnerStrideAtCompileTime == Eigen::Dynamic
+                        && S::OuterStrideAtCompileTime != Eigen::Dynamic
+                        && std::is_constructible<S, EigenIndex>::value>;
+
+    template <typename S = StrideType, enable_if_t<stride_ctor_default<S>::value, int> = 0>
+    static S make_stride(EigenIndex, EigenIndex) {
+        return S();
+    }
+    template <typename S = StrideType, enable_if_t<stride_ctor_dual<S>::value, int> = 0>
+    static S make_stride(EigenIndex outer, EigenIndex inner) {
+        return S(outer, inner);
+    }
+    template <typename S = StrideType, enable_if_t<stride_ctor_outer<S>::value, int> = 0>
+    static S make_stride(EigenIndex outer, EigenIndex) {
+        return S(outer);
+    }
+    template <typename S = StrideType, enable_if_t<stride_ctor_inner<S>::value, int> = 0>
+    static S make_stride(EigenIndex, EigenIndex inner) {
+        return S(inner);
+    }
+};
+
+// type_caster for special matrix types (e.g. DiagonalMatrix), which are EigenBase, but not
+// EigenDense (i.e. they don't have a data(), at least not with the usual matrix layout).
+// load() is not supported, but we can cast them into the python domain by first copying to a
+// regular Eigen::Matrix, then casting that.
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_other<Type>::value>> {
+    static_assert(!std::is_pointer<typename Type::Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+
+protected:
+    using Matrix
+        = Eigen::Matrix<typename Type::Scalar, Type::RowsAtCompileTime, Type::ColsAtCompileTime>;
+    using props = EigenProps<Matrix>;
+
+public:
+    static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
+        handle h = eigen_encapsulate<props>(new Matrix(src));
+        return h;
+    }
+    static handle cast(const Type *src, return_value_policy policy, handle parent) {
+        return cast(*src, policy, parent);
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
+    // types but not bound arguments).  We still provide them (with an explicitly delete) so that
+    // you end up here if you try anyway.
+    bool load(handle, bool) = delete;
+    operator Type() = delete;
+    template <typename>
+    using cast_op_type = Type;
+};
+
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_sparse<Type>::value>> {
+    using Scalar = typename Type::Scalar;
+    static_assert(!std::is_pointer<Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using StorageIndex = remove_reference_t<decltype(*std::declval<Type>().outerIndexPtr())>;
+    using Index = typename Type::Index;
+    static constexpr bool rowMajor = Type::IsRowMajor;
+
+    bool load(handle src, bool) {
+        if (!src) {
+            return false;
+        }
+
+        auto obj = reinterpret_borrow<object>(src);
+        object sparse_module = module_::import("scipy.sparse");
+        object matrix_type = sparse_module.attr(rowMajor ? "csr_matrix" : "csc_matrix");
+
+        if (!type::handle_of(obj).is(matrix_type)) {
+            try {
+                obj = matrix_type(obj);
+            } catch (const error_already_set &) {
+                return false;
+            }
+        }
+
+        auto values = array_t<Scalar>((object) obj.attr("data"));
+        auto innerIndices = array_t<StorageIndex>((object) obj.attr("indices"));
+        auto outerIndices = array_t<StorageIndex>((object) obj.attr("indptr"));
+        auto shape = pybind11::tuple((pybind11::object) obj.attr("shape"));
+        auto nnz = obj.attr("nnz").cast<Index>();
+
+        if (!values || !innerIndices || !outerIndices) {
+            return false;
+        }
+
+        value = EigenMapSparseMatrix<Scalar,
+                                     Type::Flags &(Eigen::RowMajor | Eigen::ColMajor),
+                                     StorageIndex>(shape[0].cast<Index>(),
+                                                   shape[1].cast<Index>(),
+                                                   std::move(nnz),
+                                                   outerIndices.mutable_data(),
+                                                   innerIndices.mutable_data(),
+                                                   values.mutable_data());
+
+        return true;
+    }
+
+    static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
+        const_cast<Type &>(src).makeCompressed();
+
+        object matrix_type
+            = module_::import("scipy.sparse").attr(rowMajor ? "csr_matrix" : "csc_matrix");
+
+        array data(src.nonZeros(), src.valuePtr());
+        array outerIndices((rowMajor ? src.rows() : src.cols()) + 1, src.outerIndexPtr());
+        array innerIndices(src.nonZeros(), src.innerIndexPtr());
+
+        return matrix_type(pybind11::make_tuple(
+                               std::move(data), std::move(innerIndices), std::move(outerIndices)),
+                           pybind11::make_tuple(src.rows(), src.cols()))
+            .release();
+    }
+
+    PYBIND11_TYPE_CASTER(Type,
+                         const_name<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[",
+                                                             "scipy.sparse.csc_matrix[")
+                             + npy_format_descriptor<Scalar>::name + const_name("]"));
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)

patch/eigen/tensor.h

+/*
+    pybind11/eigen/tensor.h: Transparent conversion for Eigen tensors
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "../numpy.h"
+#include "common.h"
+
+#if defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER)
+static_assert(__GNUC__ > 5, "Eigen Tensor support in pybind11 requires GCC > 5.0");
+#endif
+
+// Disable warnings for Eigen
+PYBIND11_WARNING_PUSH
+PYBIND11_WARNING_DISABLE_MSVC(4554)
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+#if defined(__MINGW32__)
+PYBIND11_WARNING_DISABLE_GCC("-Wmaybe-uninitialized")
+#endif
+
+#include <unsupported/Eigen/CXX11/Tensor>
+
+PYBIND11_WARNING_POP
+
+static_assert(EIGEN_VERSION_AT_LEAST(3, 3, 0),
+              "Eigen Tensor support in pybind11 requires Eigen >= 3.3.0");
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline bool is_tensor_aligned(const void *data) {
+    return (reinterpret_cast<std::size_t>(data) % EIGEN_DEFAULT_ALIGN_BYTES) == 0;
+}
+
+template <typename T>
+constexpr int compute_array_flag_from_tensor() {
+    static_assert((static_cast<int>(T::Layout) == static_cast<int>(Eigen::RowMajor))
+                      || (static_cast<int>(T::Layout) == static_cast<int>(Eigen::ColMajor)),
+                  "Layout must be row or column major");
+    return (static_cast<int>(T::Layout) == static_cast<int>(Eigen::RowMajor)) ? array::c_style
+                                                                              : array::f_style;
+}
+
+template <typename T>
+struct eigen_tensor_helper {};
+
+template <typename Scalar_, int NumIndices_, int Options_, typename IndexType>
+struct eigen_tensor_helper<Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType>> {
+    using Type = Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType>;
+    using ValidType = void;
+
+    static Eigen::DSizes<typename Type::Index, Type::NumIndices> get_shape(const Type &f) {
+        return f.dimensions();
+    }
+
+    static constexpr bool
+    is_correct_shape(const Eigen::DSizes<typename Type::Index, Type::NumIndices> & /*shape*/) {
+        return true;
+    }
+
+    template <typename T>
+    struct helper {};
+
+    template <size_t... Is>
+    struct helper<index_sequence<Is...>> {
+        static constexpr auto value = concat(const_name(((void) Is, "?"))...);
+    };
+
+    static constexpr auto dimensions_descriptor
+        = helper<decltype(make_index_sequence<Type::NumIndices>())>::value;
+
+    template <typename... Args>
+    static Type *alloc(Args &&...args) {
+        return new Type(std::forward<Args>(args)...);
+    }
+
+    static void free(Type *tensor) { delete tensor; }
+};
+
+template <typename Scalar_, typename std::ptrdiff_t... Indices, int Options_, typename IndexType>
+struct eigen_tensor_helper<
+    Eigen::TensorFixedSize<Scalar_, Eigen::Sizes<Indices...>, Options_, IndexType>> {
+    using Type = Eigen::TensorFixedSize<Scalar_, Eigen::Sizes<Indices...>, Options_, IndexType>;
+    using ValidType = void;
+
+    static constexpr Eigen::DSizes<typename Type::Index, Type::NumIndices>
+    get_shape(const Type & /*f*/) {
+        return get_shape();
+    }
+
+    static constexpr Eigen::DSizes<typename Type::Index, Type::NumIndices> get_shape() {
+        return Eigen::DSizes<typename Type::Index, Type::NumIndices>(Indices...);
+    }
+
+    static bool
+    is_correct_shape(const Eigen::DSizes<typename Type::Index, Type::NumIndices> &shape) {
+        return get_shape() == shape;
+    }
+
+    static constexpr auto dimensions_descriptor = concat(const_name<Indices>()...);
+
+    template <typename... Args>
+    static Type *alloc(Args &&...args) {
+        Eigen::aligned_allocator<Type> allocator;
+        return ::new (allocator.allocate(1)) Type(std::forward<Args>(args)...);
+    }
+
+    static void free(Type *tensor) {
+        Eigen::aligned_allocator<Type> allocator;
+        tensor->~Type();
+        allocator.deallocate(tensor, 1);
+    }
+};
+
+template <typename Type, bool ShowDetails, bool NeedsWriteable = false>
+struct get_tensor_descriptor {
+    static constexpr auto details
+        = const_name<NeedsWriteable>(", flags.writeable", "")
+          + const_name<static_cast<int>(Type::Layout) == static_cast<int>(Eigen::RowMajor)>(
+              ", flags.c_contiguous", ", flags.f_contiguous");
+    static constexpr auto value
+        = const_name("numpy.ndarray[") + npy_format_descriptor<typename Type::Scalar>::name
+          + const_name("[") + eigen_tensor_helper<remove_cv_t<Type>>::dimensions_descriptor
+          + const_name("]") + const_name<ShowDetails>(details, const_name("")) + const_name("]");
+};
+
+// When EIGEN_AVOID_STL_ARRAY is defined, Eigen::DSizes<T, 0> does not have the begin() member
+// function. Falling back to a simple loop works around this issue.
+//
+// We need to disable the type-limits warning for the inner loop when size = 0.
+
+PYBIND11_WARNING_PUSH
+PYBIND11_WARNING_DISABLE_GCC("-Wtype-limits")
+
+template <typename T, int size>
+std::vector<T> convert_dsizes_to_vector(const Eigen::DSizes<T, size> &arr) {
+    std::vector<T> result(size);
+
+    for (size_t i = 0; i < size; i++) {
+        result[i] = arr[i];
+    }
+
+    return result;
+}
+
+template <typename T, int size>
+Eigen::DSizes<T, size> get_shape_for_array(const array &arr) {
+    Eigen::DSizes<T, size> result;
+    const T *shape = arr.shape();
+    for (size_t i = 0; i < size; i++) {
+        result[i] = shape[i];
+    }
+
+    return result;
+}
+
+PYBIND11_WARNING_POP
+
+template <typename Type>
+struct type_caster<Type, typename eigen_tensor_helper<Type>::ValidType> {
+    static_assert(!std::is_pointer<typename Type::Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using Helper = eigen_tensor_helper<Type>;
+    static constexpr auto temp_name = get_tensor_descriptor<Type, false>::value;
+    PYBIND11_TYPE_CASTER(Type, temp_name);
+
+    bool load(handle src, bool convert) {
+        if (!convert) {
+            if (!isinstance<array>(src)) {
+                return false;
+            }
+            array temp = array::ensure(src);
+            if (!temp) {
+                return false;
+            }
+
+            if (!temp.dtype().is(dtype::of<typename Type::Scalar>())) {
+                return false;
+            }
+        }
+
+        array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()> arr(
+            reinterpret_borrow<object>(src));
+
+        if (arr.ndim() != Type::NumIndices) {
+            return false;
+        }
+        auto shape = get_shape_for_array<typename Type::Index, Type::NumIndices>(arr);
+
+        if (!Helper::is_correct_shape(shape)) {
+            return false;
+        }
+
+#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
+        auto data_pointer = arr.data();
+#else
+        // Handle Eigen bug
+        auto data_pointer = const_cast<typename Type::Scalar *>(arr.data());
+#endif
+
+        if (is_tensor_aligned(arr.data())) {
+            value = Eigen::TensorMap<const Type, Eigen::Aligned>(data_pointer, shape);
+        } else {
+            value = Eigen::TensorMap<const Type>(data_pointer, shape);
+        }
+
+        return true;
+    }
+
+    static handle cast(Type &&src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::reference
+            || policy == return_value_policy::reference_internal) {
+            pybind11_fail("Cannot use a reference return value policy for an rvalue");
+        }
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+
+    static handle cast(const Type &&src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::reference
+            || policy == return_value_policy::reference_internal) {
+            pybind11_fail("Cannot use a reference return value policy for an rvalue");
+        }
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+
+    static handle cast(Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(const Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(&src, policy, parent);
+    }
+
+    static handle cast(Type *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    static handle cast(const Type *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    template <typename C>
+    static handle cast_impl(C *src, return_value_policy policy, handle parent) {
+        object parent_object;
+        bool writeable = false;
+        switch (policy) {
+            case return_value_policy::move:
+                if (std::is_const<C>::value) {
+                    pybind11_fail("Cannot move from a constant reference");
+                }
+
+                src = Helper::alloc(std::move(*src));
+
+                parent_object
+                    = capsule(src, [](void *ptr) { Helper::free(reinterpret_cast<Type *>(ptr)); });
+                writeable = true;
+                break;
+
+            case return_value_policy::take_ownership:
+                if (std::is_const<C>::value) {
+                    // This cast is ugly, and might be UB in some cases, but we don't have an
+                    // alternative here as we must free that memory
+                    Helper::free(const_cast<Type *>(src));
+                    pybind11_fail("Cannot take ownership of a const reference");
+                }
+
+                parent_object
+                    = capsule(src, [](void *ptr) { Helper::free(reinterpret_cast<Type *>(ptr)); });
+                writeable = true;
+                break;
+
+            case return_value_policy::copy:
+                writeable = true;
+                break;
+
+            case return_value_policy::reference:
+                parent_object = none();
+                writeable = !std::is_const<C>::value;
+                break;
+
+            case return_value_policy::reference_internal:
+                // Default should do the right thing
+                if (!parent) {
+                    pybind11_fail("Cannot use reference internal when there is no parent");
+                }
+                parent_object = reinterpret_borrow<object>(parent);
+                writeable = !std::is_const<C>::value;
+                break;
+
+            default:
+                pybind11_fail("pybind11 bug in eigen.h, please file a bug report");
+        }
+
+        auto result = array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()>(
+            convert_dsizes_to_vector(Helper::get_shape(*src)), src->data(), parent_object);
+
+        if (!writeable) {
+            array_proxy(result.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
+        }
+
+        return result.release();
+    }
+};
+
+template <typename StoragePointerType,
+          bool needs_writeable,
+          enable_if_t<!needs_writeable, bool> = true>
+StoragePointerType get_array_data_for_type(array &arr) {
+#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
+    return reinterpret_cast<StoragePointerType>(arr.data());
+#else
+    // Handle Eigen bug
+    return reinterpret_cast<StoragePointerType>(const_cast<void *>(arr.data()));
+#endif
+}
+
+template <typename StoragePointerType,
+          bool needs_writeable,
+          enable_if_t<needs_writeable, bool> = true>
+StoragePointerType get_array_data_for_type(array &arr) {
+    return reinterpret_cast<StoragePointerType>(arr.mutable_data());
+}
+
+template <typename T, typename = void>
+struct get_storage_pointer_type;
+
+template <typename MapType>
+struct get_storage_pointer_type<MapType, void_t<typename MapType::StoragePointerType>> {
+    using SPT = typename MapType::StoragePointerType;
+};
+
+template <typename MapType>
+struct get_storage_pointer_type<MapType, void_t<typename MapType::PointerArgType>> {
+    using SPT = typename MapType::PointerArgType;
+};
+
+template <typename Type, int Options>
+struct type_caster<Eigen::TensorMap<Type, Options>,
+                   typename eigen_tensor_helper<remove_cv_t<Type>>::ValidType> {
+    static_assert(!std::is_pointer<typename Type::Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using MapType = Eigen::TensorMap<Type, Options>;
+    using Helper = eigen_tensor_helper<remove_cv_t<Type>>;
+
+    bool load(handle src, bool /*convert*/) {
+        // Note that we have a lot more checks here as we want to make sure to avoid copies
+        if (!isinstance<array>(src)) {
+            return false;
+        }
+        auto arr = reinterpret_borrow<array>(src);
+        if ((arr.flags() & compute_array_flag_from_tensor<Type>()) == 0) {
+            return false;
+        }
+
+        if (!arr.dtype().is(dtype::of<typename Type::Scalar>())) {
+            return false;
+        }
+
+        if (arr.ndim() != Type::NumIndices) {
+            return false;
+        }
+
+        constexpr bool is_aligned = (Options & Eigen::Aligned) != 0;
+
+        if (is_aligned && !is_tensor_aligned(arr.data())) {
+            return false;
+        }
+
+        auto shape = get_shape_for_array<typename Type::Index, Type::NumIndices>(arr);
+
+        if (!Helper::is_correct_shape(shape)) {
+            return false;
+        }
+
+        if (needs_writeable && !arr.writeable()) {
+            return false;
+        }
+
+        auto result = get_array_data_for_type<typename get_storage_pointer_type<MapType>::SPT,
+                                              needs_writeable>(arr);
+
+        value.reset(new MapType(std::move(result), std::move(shape)));
+
+        return true;
+    }
+
+    static handle cast(MapType &&src, return_value_policy policy, handle parent) {
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(const MapType &&src, return_value_policy policy, handle parent) {
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(MapType &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(const MapType &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(&src, policy, parent);
+    }
+
+    static handle cast(MapType *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    static handle cast(const MapType *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    template <typename C>
+    static handle cast_impl(C *src, return_value_policy policy, handle parent) {
+        object parent_object;
+        constexpr bool writeable = !std::is_const<C>::value;
+        switch (policy) {
+            case return_value_policy::reference:
+                parent_object = none();
+                break;
+
+            case return_value_policy::reference_internal:
+                // Default should do the right thing
+                if (!parent) {
+                    pybind11_fail("Cannot use reference internal when there is no parent");
+                }
+                parent_object = reinterpret_borrow<object>(parent);
+                break;
+
+            case return_value_policy::take_ownership:
+                delete src;
+                // fallthrough
+            default:
+                // move, take_ownership don't make any sense for a ref/map:
+                pybind11_fail("Invalid return_value_policy for Eigen Map type, must be either "
+                              "reference or reference_internal");
+        }
+
+        auto result = array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()>(
+            convert_dsizes_to_vector(Helper::get_shape(*src)),
+            src->data(),
+            std::move(parent_object));
+
+        if (!writeable) {
+            array_proxy(result.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
+        }
+
+        return result.release();
+    }
+
+#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
+
+    static constexpr bool needs_writeable = !std::is_const<typename std::remove_pointer<
+        typename get_storage_pointer_type<MapType>::SPT>::type>::value;
+#else
+    // Handle Eigen bug
+    static constexpr bool needs_writeable = !std::is_const<Type>::value;
+#endif
+
+protected:
+    // TODO: Move to std::optional once std::optional has more support
+    std::unique_ptr<MapType> value;
+
+public:
+    static constexpr auto name = get_tensor_descriptor<Type, true, needs_writeable>::value;
+    explicit operator MapType *() { return value.get(); }
+    explicit operator MapType &() { return *value; }
+    explicit operator MapType &&() && { return std::move(*value); }
+
+    template <typename T_>
+    using cast_op_type = ::pybind11::detail::movable_cast_op_type<T_>;
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)

readme.md

@@ -17,8 +17,14 @@ pip install .
 ```
 
 #### Trouble Shooting
-* `fatal error: eigen/matrix.h: No such file or directory`:
-This is a known issue for `torch==2.1.0` and `torch==2.1.1` (https://github.com/pytorch/pytorch/issues/112841). Please avoid using this two versions.
+**`fatal error: eigen/matrix.h: No such file or directory`**
+
+This is a known issue for `torch==2.1.0` and `torch==2.1.1` (https://github.com/pytorch/pytorch/issues/112841). 
+To patch up these two versions, clone this repository, and copy `patch/eigen` to your pytorch include directory:
+```bash
+# for example, if you are using anaconda (assume base env)
+cp -r patch/eigen ~/anaconda3/lib/python3.9/site-packages/torch/include/pybind11/
+```
 
 ### Usage