#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
#pragma once

#include <c10/core/Allocator.h>
#include <c10/core/Device.h>
#include <c10/core/DeviceType.h>
#include <c10/core/DispatchKey.h>
#include <c10/core/DispatchKeySet.h>
#include <c10/core/InferenceMode.h>
#include <c10/core/Layout.h>
#include <c10/core/MemoryFormat.h>
#include <c10/core/SafePyObject.h>
#include <c10/core/ScalarType.h>
#include <c10/core/ScalarTypeToTypeMeta.h>
#include <c10/core/Storage.h>
#include <c10/core/SymBool.h>
#include <c10/core/SymInt.h>
#include <c10/core/SymIntArrayRef.h>
#include <c10/core/SymbolicShapeMeta.h>
#include <c10/core/WrapDimMinimal.h>
#include <c10/core/impl/PyObjectSlot.h>
#include <c10/core/impl/SizesAndStrides.h>
#include <c10/macros/Export.h>
#include <c10/macros/Macros.h>
#include <c10/util/ArrayRef.h>
#include <c10/util/DimVector.h>
#include <c10/util/Exception.h>
#include <c10/util/Flags.h>
#include <c10/util/accumulate.h>
#include <c10/util/intrusive_ptr.h>
#include <c10/util/irange.h>
#include <c10/util/safe_numerics.h>
#include <c10/util/typeid.h>
#include <optional>

#include <algorithm>
#include <atomic>
#include <cstddef>
#include <cstdint>
#include <limits>
#include <memory>
#include <string>
#include <type_traits>
#include <utility>
#include <vector>

// A global boolean variable to control whether we free memory when a Tensor
// is shrunk to a smaller size. As a result, a Tensor is always going to
// keep the memory allocated for its maximum capacity reshaped to so far.
//
// This parameter is respected "upper-case" methods which call Resize()
// (e.g., CopyFrom, ResizeLike); it is NOT respected by Tensor::resize_
// or ShrinkTo, both of which guarantee to never to free memory.
C10_DECLARE_bool(caffe2_keep_on_shrink);

// Since we can have high variance in blob memory allocated across different
// inputs in the same run, we will shrink the blob only if the memory gain
// is larger than this flag in bytes.  This only applies to functions which
// respect caffe2_keep_on_shrink.
C10_DECLARE_int64(caffe2_max_keep_on_shrink_memory);

C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-default")

namespace at {
class Tensor;
class TensorBase;
} // namespace at

namespace c10 {

/**
 * A utility function to convert vector<int> to vector<int64_t>.
 */
inline std::vector<int64_t> ToVectorint64_t(const ArrayRef<int>& src) {
  return std::vector<int64_t>(src.begin(), src.end());
}

/**
 * Return product of all dimensions starting from k
 */
inline int64_t size_from_dim_(int k, IntArrayRef dims) {
  int64_t r = 1;
  for (const auto i : c10::irange(k, dims.size())) {
    r *= dims[i];
  }
  return r;
}

// Product of all dims up to k (not including dims[k])
inline int64_t size_to_dim_(int k, IntArrayRef dims) {
  TORCH_CHECK(k >= 0 && static_cast<size_t>(k) <= dims.size());
  int64_t r = 1;
  for (const auto i : c10::irange(k)) {
    r *= dims[i];
  }
  return r;
}

// Product of all dims between k and l (not including dims[k] and dims[l])
inline int64_t size_between_dim_(int k, int l, IntArrayRef dims) {
  TORCH_CHECK((unsigned)l < dims.size() && (unsigned)k < dims.size());
  int64_t r = 1;
  if (k < l) {
    for (int i = k + 1; i < l; ++i) {
      r *= dims[i];
    }
  } else {
    for (int i = l + 1; i < k; ++i) {
      r *= dims[i];
    }
  }
  return r;
}

// Wrap around axis_index if it is negative, s.t., -1 is the last dim
inline int canonical_axis_index_(int axis_index, int ndims) {
  TORCH_CHECK(axis_index >= -ndims);
  TORCH_CHECK(axis_index < ndims);
  if (axis_index < 0) {
    return axis_index + ndims;
  }
  return axis_index;
}

using PlacementDtor = void (*)(void*, size_t);

/*
 * A Context that will call extra placement deleter during
 * deconstruction.
 *
 * Accept a already constructed DataPtr and store it as member
 * during destruction, we'll call extra deleter on the underlying
 * data pointer before the DataPtr is destructed.
 * `data_ptr_` owns the memory.
 */
struct C10_API PlacementDeleteContext {
  DataPtr data_ptr_;
  PlacementDtor placement_dtor_;
  size_t size_;

  PlacementDeleteContext(
      DataPtr&& data_ptr,
      PlacementDtor placement_dtor,
      size_t size)
      : data_ptr_(std::move(data_ptr)),
        placement_dtor_(placement_dtor),
        size_(size) {}

  PlacementDeleteContext(PlacementDeleteContext&&) noexcept = delete;
  PlacementDeleteContext(const PlacementDeleteContext&) = delete;
  PlacementDeleteContext& operator=(const PlacementDeleteContext&) = delete;
  PlacementDeleteContext& operator=(PlacementDeleteContext&&) = delete;
  static DataPtr makeDataPtr(
      DataPtr&& data_ptr,
      PlacementDtor placement_dtor,
      size_t size,
      Device device);
  ~PlacementDeleteContext() {
    placement_dtor_(data_ptr_.get(), size_);
    // original memory will be freed when data_ptr_ is destructed
  }
};

struct C10_API AutogradMetaInterface {
  virtual void set_requires_grad(
      bool requires_grad,
      at::TensorImpl* self_impl) = 0;
  virtual bool requires_grad() const = 0;
  virtual at::Tensor& mutable_grad() = 0;
  virtual const at::Tensor& grad() const = 0;
  virtual const at::Tensor& fw_grad(uint64_t level, const at::TensorBase& self)
      const = 0;
  virtual void set_fw_grad(
      const at::TensorBase& new_grad,
      const at::TensorBase& self,
      uint64_t level,
      bool is_inplace_op) = 0;
  virtual ~AutogradMetaInterface();
};

namespace impl {

// Unfortunately, the definition of AutogradMeta lives in a separate
// compilation unit than TensorImpl (libtorch.so versus libc10.so)
// which means that we cannot construct an AutogradMeta from TensorImpl,
// not even from the cpp file.  So we have to indirect it through a factory
// function which will be initialized when we load libtorch.so.

struct C10_API AutogradMetaFactory {
  virtual ~AutogradMetaFactory() = default;
  virtual std::unique_ptr<AutogradMetaInterface> make() const = 0;
  // This method is the dumbest method.  But I don't have access
  // to Tensor (not TensorImpl) which is undefined in this header.
  virtual const at::Tensor& undefined_tensor() const = 0;
};

C10_API void SetAutogradMetaFactory(AutogradMetaFactory* factory);
C10_API AutogradMetaFactory* GetAutogradMetaFactory();

struct C10_API AutogradMetaFactoryRegisterer{
    explicit AutogradMetaFactoryRegisterer(AutogradMetaFactory * factory){
        SetAutogradMetaFactory(factory);
} // namespace impl
}; // namespace c10

} // namespace impl

// For ease of copy pasting
#if 0
is_contiguous
is_channels_last_contiguous
is_channels_last_3d_contiguous
is_channels_last
is_channels_last_3d
is_non_overlapping_and_dense
#endif

/**
 * This structure is intended to hold additional metadata of the specific device
 * backend.
 **/
struct C10_API BackendMeta : intrusive_ptr_target {
  ~BackendMeta() override = default;
  virtual intrusive_ptr<BackendMeta> clone(
      const intrusive_ptr<BackendMeta>& ptr) const {
    return ptr;
  }
};

// same as Python's FakeTensorMode
// storing shape env and converter from Python, we'll use these later
// to implement sym ints, real tensor conversion, etc
// this doesn't have caching because we're not implementing it
// no in_kernel_invocation_manager since that's handled by dispatch keys in C++
struct C10_API FakeTensorMode {
  std::shared_ptr<c10::SafePyObject> shape_env_;
  std::shared_ptr<c10::SafePyObject> fake_tensor_converter_;

  FakeTensorMode(
      std::shared_ptr<c10::SafePyObject> shape_env,
      std::shared_ptr<c10::SafePyObject> converter)
      : shape_env_(std::move(shape_env)),
        fake_tensor_converter_(std::move(converter)) {}
};

struct C10_API ExtraMeta {
  std::unique_ptr<c10::SymbolicShapeMeta> symbolic_shape_meta_ = nullptr;
  intrusive_ptr<c10::BackendMeta> backend_meta_ = nullptr;
  std::optional<std::string> custom_data_ptr_error_msg_ = std::nullopt;
  std::optional<std::string> custom_storage_error_msg_ = std::nullopt;
  std::optional<c10::Device> fake_device_ = std::nullopt;
  std::shared_ptr<FakeTensorMode> fake_tensor_mode_ = nullptr;

  ExtraMeta() = default;
  ~ExtraMeta() = default;
  ExtraMeta(const ExtraMeta& other) {
    if (other.symbolic_shape_meta_) {
      symbolic_shape_meta_ =
          std::make_unique<c10::SymbolicShapeMeta>(*other.symbolic_shape_meta_);
    }
    if (other.backend_meta_) {
      backend_meta_ = other.backend_meta_->clone(other.backend_meta_);
    }
    custom_data_ptr_error_msg_ = other.custom_data_ptr_error_msg_;
    custom_storage_error_msg_ = other.custom_storage_error_msg_;
    fake_device_ = other.fake_device_;
    fake_tensor_mode_ = other.fake_tensor_mode_;
  }
  ExtraMeta& operator=(const ExtraMeta& other) = delete;
  ExtraMeta(ExtraMeta&& other) = delete;
  ExtraMeta& operator=(ExtraMeta&& other) = delete;

  ExtraMeta(
      std::unique_ptr<c10::SymbolicShapeMeta> symbolic_shape_meta,
      intrusive_ptr<c10::BackendMeta> backend_meta,
      std::optional<std::string> custom_data_ptr_error_msg = std::nullopt,
      std::optional<std::string> custom_storage_access_error_msg = std::nullopt)
      : symbolic_shape_meta_(std::move(symbolic_shape_meta)),
        backend_meta_(std::move(backend_meta)),
        custom_data_ptr_error_msg_(std::move(custom_data_ptr_error_msg)),
        custom_storage_error_msg_(std::move(custom_storage_access_error_msg)) {}

  std::unique_ptr<ExtraMeta> clone() const {
    return std::make_unique<ExtraMeta>(*this);
  }
};

// NOTE [ Version Counter Sharing ]
//
// Every Tensor has a version counter. Version counters are incremented whenever
// the data or size of a tensor changes through in-place Variable operations.
// Version counters are used to detect modifications to saved variables which
// would result in incorrect gradient calculations. Version counters may be
// shared between Variables:
//
// 1. A view shares the version counter of the base Variable,
// 2. `x.detach()` shares the version counter of `x`,
// 3. Unpacked saved variables share the version counter of the source.
//
// Version counters are not shared in these scenarios:
//
// 1. When we replace a `Variable`'s underlying `Tensor` by calling
// `set_data(...)`,
// 2. `x.data` does not share the version counter of `x`. (See discussion at
// https://github.com/pytorch/pytorch/issues/5396)
//
// Question: Why do we put the version counter in TensorImpl instead of
// AutogradMeta?
//
// Answer: After the Variable/Tensor merge, a tensor will not have AutogradMeta
// when its `requires_grad_` is false, but when we use this tensor in the
// forward pass of a function that requires saving this tensor for backward, we
// need to keep track of this tensor's version to make sure it's always valid in
// the autograd graph.
//
// To achieve this goal, we put the version counter in TensorImpl instead of
// AutogradMeta, and have it always be available. This allows us to have the
// optimization of not carrying AutogradMeta when a tensor doesn't require
// gradient.
//
// A hypothetical alternative way to achieve this goal is to initialize
// AutogradMeta and create the version counter for the non-requires-grad tensor
// only when it's saved for backward. However, since saving a tensor for
// backward happens in the forward pass, and our invariant is that forward pass
// needs to be thread-safe, lazy-initializing AutogradMeta when saving a tensor
// can introduce race conditions when we are running the forward pass in
// multi-thread scenarios, thus making the forward pass not thread-safe anymore,
// which breaks the invariant.
struct C10_API VariableVersion {
 private:
  struct VersionCounter : intrusive_ptr_target {
    VersionCounter(uint32_t version) : version_(version) {}
    std::atomic<uint32_t> version_;
  };
  c10::intrusive_ptr<VersionCounter> version_counter_;

 public:
  // Note [Disabled VariableVersion]
  // VariableVersion struct has an intrusive_ptr pointing VersionCounter struct
  // with an atomic variable. Thus `VariableVersion(/*version=*/0)` is not as
  // cheap as we expected. In some cases constructing a VariableVersion with
  // version 0 is not necessary so we add a cheap constructor which
  // doesn't allocate the intrusive_ptr.
  // Example use cases are:
  //  - Inference tensors don't track version counter, so they'll just always
  //    have disabled VariableVersion.
  //  - In SavedVariable class we override version_counter_ inside its
  //  constructor
  //    so that we can use the cheap constructor there.
  enum Disabled { DISABLED };
  // It's okay to return true even for inference tensor which
  // doesn't have version counter enabled.
  // We want to be permissive here since in many cases (e.g. make_variable)
  // we can std::move a TensorImpl if there's no other uses which saves us
  // an additional TensorImpl allocation.
  bool unique() const {
    return version_counter_ ? 1 == version_counter_.use_count() : true;
  }
  // NOTE: As of C++11 and 14, default-constructing a std::atomic variable
  // leaves it in a persistently undefined state. See
  // https://cplusplus.github.io/LWG/issue2334.
  VariableVersion(uint32_t version)
      : version_counter_(c10::make_intrusive<VersionCounter>(version)) {}
  VariableVersion(Disabled /*unused*/ = DISABLED) {}

  bool enabled() const {
    return version_counter_;
  }

  // Note [Inplace update inference tensor]
  // 1. Inplace update to inference tensor is forbidden in normal mode.
  //   For example:
  //     inference_tensor.copy_(normal_tensor_requires_grad)
  //   This inplace makes inference_tensor have requires_grad=True and
  //   have a grad_fn.  This is bad because views of `inference_tensor`
  //   created in InferenceMode won't be able to know the grad_fn since
  //   their ViewMeta were not recorded. To match NoGradMode behavior
  //   that "inplace update to a view created in NoGradMode raise an error",
  //   we just ban inplace update to inference tensor since we can't tell
  //   if an inference tensor is a view created in InferenceMode.
  //
  //   Note that views of normal tensor created in InferenceMode has proper
  //   ViewMeta so that they're aware of the grad_fn correctly.
  //
  // 2. Inplace update to inference tensor in inference tensor doesn't bump
  //    version counter.
  //    * It either doesn't call bump() by skipping ADInplaceOrView kernel,
  //      - e.g. inference_tensor.add_(1)
  //    * or bump() is a no-op for inference tensor.
  //      - e.g. inference_tensor.add_(normal_tensor)
  void bump() {
    // TODO: Replace the link to the documentation once it's available.
    TORCH_CHECK(
        version_counter_ || InferenceMode::is_enabled(),
        "Inplace update to inference tensor outside InferenceMode is not allowed."
        "You can make a clone to get a normal tensor before doing inplace update."
        "See https://github.com/pytorch/rfcs/pull/17 for more details.");
    if (version_counter_) {
      ++version_counter_->version_;
    }
  }

  void set_version(int64_t i) {
    TORCH_CHECK(
        version_counter_,
        "Tried to call torch.autograd._unsafe_set_version() on a tensor "
        "that does not have a version counter. Was it created in inference mode?");
    TORCH_CHECK(i >= 0, "Cannot set a version_counter to a value below 0: ", i);
    version_counter_->version_ = i;
  }

  // Inference tensor doesn't have version counter so it shouldn't be
  // accessed.
  uint32_t current_version() const {
    TORCH_CHECK(
        version_counter_, "Inference tensors do not track version counter.");
    return version_counter_->version_;
  }
};

// Forward declaration of TensorImpl needed for forward declaration of
// C10_TensorImpl_Size_Check_Dummy_Class
struct C10_API TensorImpl;

/**
 * NOTE: Some TensorImpl methods are small and not overridden in the
 * PyTorch codebase itself, but may theoretically need to be
 * overridden by third-party TensorImpl subclasses. This macro allows
 * users that need maximum performance and don't need these extension
 * points to disable them with a build-time flag. (In particular,
 * XLA's XLATensorImpl currently overrides these methods, so we can't
 * enable this flag by default.)
 */
#ifdef C10_DISABLE_TENSORIMPL_EXTENSIBILITY
#define TENSORIMPL_MAYBE_VIRTUAL
#else
#define TENSORIMPL_MAYBE_VIRTUAL virtual
#endif

/**
 * The low-level representation of a tensor, which contains a pointer
 * to a storage (which contains the actual data) and metadata (e.g., sizes and
 * strides) describing this particular view of the data as a tensor.
 *
 * Some basic characteristics about our in-memory representation of
 * tensors:
 *
 *  - It contains a pointer to a storage struct (Storage/StorageImpl)
 *    which contains the pointer to the actual data and records the
 *    data type and device of the view.  This allows multiple tensors
 *    to alias the same underlying data, which allows to efficiently
 *    implement differing *views* on a tensor.
 *
 *  - The tensor struct itself records view-specific metadata about
 *    the tensor, e.g., sizes, strides and offset into storage.
 *    Each view of a storage can have a different size or offset.
 *
 *  - This class is intrusively refcounted.  It is refcounted so that
 *    we can support prompt deallocation of large tensors; it is
 *    intrusively refcounted so that we can still perform reference
 *    counted operations on raw pointers, which is often more convenient
 *    when passing tensors across language boundaries.
 *
 *  - For backwards-compatibility reasons, a tensor may be in an
 *    uninitialized state.  A tensor may be uninitialized in the following
 *    two ways:
 *
 *      - A tensor may be DTYPE UNINITIALIZED.  A tensor of this
 *        form has an uninitialized dtype.  This situation most
 *        frequently arises when a user writes Tensor x(CPU).  The dtype
 *        is subsequently initialized when mutable_data<T>() is
 *        invoked for the first time.
 *
 *      - A tensor may be STORAGE UNINITIALIZED.  A tensor of this form
 *        has non-zero size, but has a storage with a null data pointer.
 *        This situation most frequently arises when a user calls
 *        Resize() or FreeMemory().  This is because Caffe2 historically
 *        does lazy allocation: allocation of data doesn't occur until
 *        mutable_data<T>() is invoked.  A tensor with zero size is
 *        always storage initialized, because no allocation is necessary
 *        in this case.
 *
 *    All combinations of these two uninitialized states are possible.
 *    Consider the following transcript in idiomatic Caffe2 API:
 *
 *      Tensor x(CPU); // x is storage-initialized, dtype-UNINITIALIZED
 *      x.Resize(4); // x is storage-UNINITIALIZED, dtype-UNINITIALIZED
 *      x.mutable_data<float>(); // x is storage-initialized, dtype-initialized
 *      x.FreeMemory(); // x is storage-UNINITIALIZED, dtype-initialized.
 *
 *    All other fields on tensor are always initialized.  In particular,
 *    size is always valid. (Historically, a tensor declared as Tensor x(CPU)
 *    also had uninitialized size, encoded as numel == -1, but we have now
 *    decided to default to zero size, resulting in numel == 0).
 *
 *    Uninitialized storages MUST be uniquely owned, to keep our model
 *    simple.  Thus, we will reject operations which could cause an
 *    uninitialized storage to become shared (or a shared storage to
 *    become uninitialized, e.g., from FreeMemory).
 *
 *    In practice, tensors which are storage-UNINITIALIZED and
 *    dtype-UNINITIALIZED are *extremely* ephemeral: essentially,
 *    after you do a Resize(), you basically always call mutable_data()
 *    immediately afterwards.  Most functions are not designed to
 *    work if given a storage-UNINITIALIZED, dtype-UNINITIALIZED tensor.
 *
 *    We intend to eliminate all uninitialized states, so that every
 *    tensor is fully initialized in all fields.  Please do not write new code
 *    that depends on these uninitialized states.
 */
struct C10_API TensorImpl : public c10::intrusive_ptr_target {
  TensorImpl() = delete;
  ~TensorImpl() override;
  // Note [Enum ImplType]
  // This enum is temporary. In the followup refactor we should
  // think about how to specialize TensorImpl creation for view
  // tensors. Currently we only special case its key_set_ but
  // there's also potential to share version_counter_ directly
  // without creating first and then override in as_view.
  enum ImplType { VIEW };

  /**
   * Construct a 1-dim 0-size tensor backed by the given storage.
   */
  TensorImpl(
      Storage&& storage,
      DispatchKeySet /*key_set*/,
      const caffe2::TypeMeta data_type);

  // See Note [Enum ImplType]
  TensorImpl(
      ImplType /*unused*/,
      Storage&& storage,
      DispatchKeySet /*key_set*/,
      const caffe2::TypeMeta data_type);

  /**
   * Construct a 1-dim 0 size tensor that doesn't have a storage.
   */
  TensorImpl(
      DispatchKeySet /*key_set*/,
      const caffe2::TypeMeta data_type,
      std::optional<c10::Device> device_opt);

  // Legacy constructors so I don't have to go update call sites.
  // TODO: When Variable is added, delete these constructors
  TensorImpl(
      Storage&& storage,
      DispatchKey dispatch_key,
      const caffe2::TypeMeta data_type)
      : TensorImpl(
            std::move(storage),
            DispatchKeySet(dispatch_key),
            data_type) {}
  TensorImpl(
      DispatchKey dispatch_key,
      const caffe2::TypeMeta data_type,
      std::optional<c10::Device> device_opt)
      : TensorImpl(DispatchKeySet(dispatch_key), data_type, device_opt) {}

 private:
  // This constructor is private, because the data_type is redundant with
  // storage.  Still, we pass it in separately because it's easier to write
  // the initializer list if we're not worried about storage being moved out
  // from under us.
  TensorImpl(
      Storage&& storage,
      DispatchKeySet /*key_set*/,
      const caffe2::TypeMeta data_type,
      std::optional<c10::Device> /*device_opt*/);

 public:
  TensorImpl(const TensorImpl&) = delete;
  TensorImpl& operator=(const TensorImpl&) = delete;
  TensorImpl(TensorImpl&&) = delete;
  TensorImpl& operator=(TensorImpl&&) = delete;

  /**
   * Release (decref) storage, and any other external allocations.  This
   * override is for `intrusive_ptr_target` and is used to implement weak
   * tensors.
   */
  void release_resources() override;

 public:
  /**
   * Return the DispatchKeySet corresponding to this Tensor, specifying
   * all of the DispatchKeys that this Tensor identifies as.  This is the
   * information used to dispatch operations on this tensor.
   */
  DispatchKeySet key_set() const {
    return key_set_;
  }

 private:
  [[noreturn]] void throw_cannot_call_with_symbolic(const char* meth) const;

  // NOTE: The general recipe for customizable methods is that the fastpath
  // function (e.g., sizes()) does an unlikely policy test, and if doesn't
  // trigger, it does the fast path implementation with no checks and going
  // directly to on-TensorImpl fields.  In particular, you never need to
  // check ExtraMeta if the policy doesn't trigger, as non-trivial ExtraMeta
  // implies the policy will always match.
  //
  // The default implementations of methods are "safe": they do extra tests
  // to make sure the internal state is consistent no matter if you are
  // doing symbolic shapes or not.  If you don't want the tests, directly
  // override the custom method (e.g., custom_sizes()) to do your preferred
  // behavior.

 public:
  /**
   * Return a reference to the sizes of this tensor.  This reference remains
   * valid as long as the tensor is live and not resized.
   */
  IntArrayRef sizes() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return sizes_custom();
    }
    return sizes_and_strides_.sizes_arrayref();
  }

  SymIntArrayRef sym_sizes() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return sym_sizes_custom();
    }
    // Sizes guaranteed to be non-negative, so unchecked cast is OK
    return c10::fromIntArrayRefKnownNonNegative(
        sizes_and_strides_.sizes_arrayref());
  }

  IntArrayRef sizes_default() const {
    if (C10_UNLIKELY(has_symbolic_sizes_strides_)) {
      throw_cannot_call_with_symbolic("sizes");
    }
    return sizes_and_strides_.sizes_arrayref();
  }

  SymIntArrayRef sym_sizes_default() const {
    if (has_symbolic_sizes_strides_) {
      return symbolic_shape_meta().sizes_;
    } else {
      // Sizes guaranteed to be non-negative, so unchecked cast is OK
      return c10::fromIntArrayRefKnownNonNegative(sizes_default());
    }
  }

  template <typename T>
  ArrayRef<T> generic_sizes() {
    static_assert(
        std::is_same_v<T, int64_t> || std::is_same_v<T, c10::SymInt>,
        "Only supports int64_t and c10::SymInt.");

    if constexpr (std::is_same_v<T, int64_t>) {
      return sizes();
    } else {
      return sym_sizes();
    }
  }

  template <typename T>
  ArrayRef<T> generic_strides() {
    static_assert(
        std::is_same_v<T, int64_t> || std::is_same_v<T, c10::SymInt>,
        "Only supports int64_t and c10::SymInt.");

    if constexpr (std::is_same_v<T, int64_t>) {
      return strides();
    } else {
      return sym_strides();
    }
  }

  template <typename T>
  T generic_storage_offset() {
    static_assert(
        std::is_same_v<T, int64_t> || std::is_same_v<T, c10::SymInt>,
        "Only supports int64_t and c10::SymInt.");

    if constexpr (std::is_same_v<T, int64_t>) {
      return storage_offset();
    } else {
      return sym_storage_offset();
    }
  }

  /**
   * The number of elements in a tensor.
   *
   * WARNING: Previously, if you were using the Caffe2 API, you could
   * test numel() == -1 to see if a tensor was uninitialized.  This
   * is no longer true; numel always accurately reports the product
   * of sizes of a tensor.
   */
  int64_t numel() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return numel_custom();
    }
    return numel_;
  }

  c10::SymInt sym_numel() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return sym_numel_custom();
    }
    return c10::SymInt(SymInt::UNCHECKED, numel_);
  }

  int64_t numel_default() const {
    if (C10_UNLIKELY(has_symbolic_sizes_strides_)) {
      throw_cannot_call_with_symbolic("numel");
    }
    return numel_;
  }

  c10::SymInt sym_numel_default() const {
    if (has_symbolic_sizes_strides_) {
      return symbolic_shape_meta().numel();
    } else {
      return c10::SymInt(SymInt::UNCHECKED, numel_);
    }
  }

  /**
   * Return the number of dimensions of this tensor.  Note that 0-dimension
   * represents a Tensor that is a Scalar, e.g., one that has a single element.
   */
  int64_t dim() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return dim_custom();
    }
    return static_cast<int64_t>(sizes_and_strides_.size());
  }

  int64_t dim_default() const {
    if (has_symbolic_sizes_strides_) {
      return static_cast<int64_t>(symbolic_shape_meta().sizes_.size());
    } else {
      return static_cast<int64_t>(sizes_and_strides_.size());
    }
  }

  /**
   * Return the offset in number of elements into the storage that this
   * tensor points to.  Most tensors have storage_offset() == 0, but,
   * for example, an index into a tensor will have a non-zero storage_offset().
   *
   * WARNING: This is NOT computed in bytes.
   */
  int64_t storage_offset() const {
    // TODO: maybe this should be toggled by strides
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return storage_offset_custom();
    }
    return storage_offset_;
  }

  c10::SymInt sym_storage_offset() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return sym_storage_offset_custom();
    }
    return c10::SymInt(SymInt::UNCHECKED, storage_offset_);
  }

  int64_t storage_offset_default() const {
    if (C10_UNLIKELY(has_symbolic_sizes_strides_)) {
      throw_cannot_call_with_symbolic("storage_offset");
    }
    return storage_offset_;
  }

  c10::SymInt sym_storage_offset_default() const {
    if (has_symbolic_sizes_strides_) {
      return symbolic_shape_meta().storage_offset_;
    } else {
      return c10::SymInt(SymInt::UNCHECKED, storage_offset_);
    }
  }

  /**
   * Return a reference to the strides of this tensor.  This reference remains
   * valid as long as the tensor is live and not restrided.
   */
  IntArrayRef strides() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return strides_custom();
    }
    return sizes_and_strides_.strides_arrayref();
  }

  c10::SymIntArrayRef sym_strides() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return sym_strides_custom();
    }
    return c10::fromIntArrayRefKnownNonNegative(strides_default());
  }

  IntArrayRef strides_default() const {
    if (C10_UNLIKELY(has_symbolic_sizes_strides_)) {
      throw_cannot_call_with_symbolic("strides");
    }
    return sizes_and_strides_.strides_arrayref();
  }

  c10::SymIntArrayRef sym_strides_default() const {
    if (has_symbolic_sizes_strides_) {
      return symbolic_shape_meta().strides_;
    } else {
      return c10::fromIntArrayRefKnownNonNegative(strides_default());
    }
  }

  c10::SymBool sym_is_contiguous(
      at::MemoryFormat memory_format = at::MemoryFormat::Contiguous) const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return sym_is_contiguous_custom(memory_format);
    }
    return sym_is_contiguous_default(memory_format);
  }

  template <typename T>
  T is_contiguous_default_impl(at::MemoryFormat memory_format) const {
    if (!has_symbolic_sizes_strides_) {
      if (memory_format == at::MemoryFormat::ChannelsLast) {
        return is_channels_last_contiguous_;
      } else if (memory_format == at::MemoryFormat::ChannelsLast3d) {
        return is_channels_last_3d_contiguous_;
      }
      return is_contiguous_;
    }

    // Handle dynamic shapes.
    const auto& symbolic = symbolic_shape_meta().is_contiguous(memory_format);

    if constexpr (std::is_same_v<T, bool>) {
      return symbolic.guard_bool(__FILE__, __LINE__);
    } else {
      return symbolic;
    }
  }

  bool is_contiguous_default(at::MemoryFormat memory_format) const {
    return is_contiguous_default_impl<bool>(memory_format);
  }

  c10::SymBool sym_is_contiguous_default(at::MemoryFormat memory_format) const {
    return is_contiguous_default_impl<c10::SymBool>(memory_format);
  }

  /**
   * Whether or not a tensor is laid out in contiguous memory.
   *
   * Tensors with non-trivial strides are not contiguous.  See
   * compute_contiguous() for the exact definition of whether or not
   * a tensor is contiguous or not.
   */
  bool is_contiguous(
      at::MemoryFormat memory_format = at::MemoryFormat::Contiguous) const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return is_contiguous_custom(memory_format);
    }
    return is_contiguous_default(memory_format);
  }

  bool is_strides_like_default(at::MemoryFormat memory_format) const {
    if (has_symbolic_sizes_strides_) {
      if (memory_format == at::MemoryFormat::ChannelsLast) {
        return symbolic_shape_meta().is_channels_last().guard_bool(
            __FILE__, __LINE__);
      } else if (memory_format == at::MemoryFormat::ChannelsLast3d) {
        return symbolic_shape_meta().is_channels_last_3d().guard_bool(
            __FILE__, __LINE__);
      } else {
        return false;
      }
    }

    if (memory_format == at::MemoryFormat::ChannelsLast) {
      return is_channels_last_;
    } else if (memory_format == at::MemoryFormat::ChannelsLast3d) {
      return is_channels_last_3d_;
    } else {
      return false;
    }
  }

  SymBool sym_is_non_overlapping_and_dense_default() const {
    if (has_symbolic_sizes_strides_) {
      return symbolic_shape_meta().is_non_overlapping_and_dense();
    } else {
      return is_non_overlapping_and_dense_;
    }
  }

  bool is_non_overlapping_and_dense_default() const {
    if (has_symbolic_sizes_strides_) {
      return sym_is_non_overlapping_and_dense_default().guard_bool(
          __FILE__, __LINE__);
    } else {
      return is_non_overlapping_and_dense_;
    }
  }

  // NB: these dim accessor functions don't have _default(), as you can use
  // sizes_default/strides_default
  /**
   * Return the size of a tensor at some dimension, wrapping the dimension if
   * necessary.
   *
   * NOTE: if you know wrapping is unnecessary, do sizes()[d] instead; it will
   * be faster
   */
  int64_t size(int64_t d) const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return size_custom(d);
    }
    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
    return sizes_and_strides_.size_at_unchecked(d);
  }

  c10::SymInt sym_size(int64_t d) const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return sym_size_custom(d);
    }
    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
    const auto sizes = this->sym_sizes();
    return sizes[d];
  }

  /**
   * Return the stride of a tensor at some dimension, wrapping the dimension
   * if necessary.
   *
   * NOTE: if you know wrapping is unnecessary, do sizes()[d] instead; it will
   * be faster
   */
  int64_t stride(int64_t d) const {
    d = maybe_wrap_dim(d, dim(), false);
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      // TODO: provide stride_custom, symmetrically with size_custom.
      // There is presently no user for it; only NestedTensor is using
      // size_custom overrideability
      return strides_custom()[d]; // unchecked (maybe_wrap_dim enforces bounds)
    }
    // Intentionally don't call default, which also handles symbolic
    return sizes_and_strides_.stride_at_unchecked(d);
  }

  enum class SizesStridesPolicy : uint8_t {
    // Default behavior, e.g., dense tensor.
    //
    // Can override: nothing
    Default = 0,
    // Customizable strides behavior, e.g., sparse tensor,
    // mkldnn tensor.
    //
    // Can override: strides(), is_contiguous()
    CustomStrides = 1,
    // Customizable sizes behavior, e.g., nested tensor
    //
    // Can override: strides(), is_contiguous(), sizes(), dim(), numel()
    CustomSizes = 2
  };

 protected:
  inline bool matches_policy(SizesStridesPolicy policy) const {
    return sizes_strides_policy_ >= static_cast<uint8_t>(policy);
  }

  inline bool matches_custom(SizesStridesPolicy policy) const {
    return custom_sizes_strides_ >= static_cast<uint8_t>(policy);
  }

  inline bool matches_python_custom(SizesStridesPolicy policy) const {
    auto r = python_custom_sizes_strides_ >= static_cast<uint8_t>(policy);
    if (r) {
      TORCH_INTERNAL_ASSERT(is_python_dispatch())
    }
    return r;
  }

  /**
   * Customization points for the functions above.  sizes_strides_policy_
   * must be set to enable these.
   *
   * NB: dim is overridable separately from sizes because it is possible
   * for a tensor to have rank, but not well defined sizes.
   */
  // sizes_strides_policy_ >= CustomStrides

  virtual bool is_strides_like_custom(at::MemoryFormat memory_format) const;

  virtual c10::SymBool sym_is_non_overlapping_and_dense_custom() const;

  bool is_non_overlapping_and_dense_custom() const {
    return sym_is_non_overlapping_and_dense_custom().guard_bool(
        __FILE__, __LINE__);
  }

  virtual c10::SymBool sym_is_contiguous_custom(
      at::MemoryFormat memory_format) const;

  bool is_contiguous_custom(at::MemoryFormat memory_format) const {
    return sym_is_contiguous_custom(memory_format)
        .guard_bool(__FILE__, __LINE__);
  }

  // sizes_strides_policy_ >= CustomSizes
  // Currently this method only exists to be overwritten by subclasses such as
  // NestedTensorImpl.
  virtual int64_t size_custom(int64_t d) const {
    // TODO: We could add support to Python dispatch here.
    // TODO: We could call into aten::size.int instead of
    // sizes_custom()[d] and enable use of the dispatcher.
    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
    return sizes_custom()[d]; // unchecked (maybe_wrap_dim enforces bounds)
  }

  virtual c10::SymInt sym_size_custom(int64_t d) const {
    // TODO: We could add support to Python dispatch here.
    // TODO: We could call into aten::size.int instead of
    // sym_sizes_custom()[d] and enable use of the dispatcher.
    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
    return sym_sizes_custom()[d]; // unchecked (maybe_wrap_dim enforces bounds)
  }

  virtual IntArrayRef sizes_custom() const;
  virtual IntArrayRef strides_custom() const;
  virtual int64_t numel_custom() const;
  virtual int64_t storage_offset_custom() const;
  virtual int64_t dim_custom() const;
  virtual Device device_custom() const;
  virtual Layout layout_custom() const;

  virtual c10::SymIntArrayRef sym_sizes_custom() const;
  virtual c10::SymIntArrayRef sym_strides_custom() const;
  virtual c10::SymInt sym_numel_custom() const;
  virtual c10::SymInt sym_storage_offset_custom() const;

 public:
/**
 * True if this tensor has storage. See storage() for details.
 */
#ifdef DEBUG
  // Allow subclasses to check that their storage_ is never getting set in debug
  // builds.
  virtual
#else
  TENSORIMPL_MAYBE_VIRTUAL
#endif
      bool
      has_storage() const
// NOTE: we devirtualize this because it arguably shouldn't be an
// error just to ask subclasses if they have storage.
// This used to throw for most subclasses, but OpaqueTensorImpl
// wanted it to successfully return false, so we went ahead and made
// it a non-error.
#ifdef C10_DISABLE_TENSORIMPL_EXTENSIBILITY
  {
    return storage_;
  }
#else
      ;
#endif

  /**
   * Return the underlying storage of a Tensor.  Multiple tensors may share
   * a single storage.  A Storage is an impoverished, Tensor-like class
   * which supports far less operations than Tensor.
   *
   * Avoid using this method if possible; try to use only Tensor APIs to perform
   * operations.
   */
  TENSORIMPL_MAYBE_VIRTUAL const Storage& storage() const {
    if (C10_UNLIKELY(storage_access_should_throw_)) {
      throw_storage_access_error();
    }
    return storage_;
  }

  /**
   * Return the underlying storage, unsafely assuming this is a basic strided
   * tensor. In cases where `storage` access would throw, this returns a
   * default-constructed Storage.
   */
  inline const Storage& unsafe_storage() const {
    return storage_;
  }

  bool unique_version() const {
    return version_counter_.unique();
  }

 protected:
  virtual Layout layout_impl() const {
    TORCH_CHECK(
        false, "layout_impl is only implemented for TensorImpl subclasses.");
  }

 public:
  // Whether a tensor is sparse COO or not.
  bool is_sparse() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    return key_set_.has_all(c10::sparse_ks);
  }

  // Whether a tensor is sparse CSR or not.
  bool is_sparse_csr() const {
    return layout() == kSparseCsr;
  }

  // Whether a tensor is sparse CSR/CSC/BSR/BSC or not.
  bool is_sparse_compressed() const {
    return key_set_.has_all(c10::sparse_csr_ks);
  }

  bool is_quantized() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    constexpr auto quantized_ks = DispatchKeySet(DispatchKey::Quantized);
    return key_set_.has_all(quantized_ks);
  }

  bool is_meta() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_meta();
    }
    return device_opt_.has_value() && device_opt_->type() == kMeta;
  }

  bool is_fake() const {
    return key_set_.has(DispatchKey::Fake);
  }

  bool is_cpu() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_cpu();
    }
    // Note: we cannot rely on dispatch keys to determine the device type
    // of a tensor, because "wrapper" tensors (like FunctionalTensorWrapper)
    // don't include backend dispatch keys.
    return device_opt_.has_value() && device_opt_->type() == kCPU;
  }

  bool is_cuda() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_cuda();
    }
    return device_opt_.has_value() && device_opt_->type() == kCUDA;
  }

  bool is_xpu() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_xpu();
    }
    return device_opt_.has_value() && device_opt_->type() == kXPU;
  }

  bool is_ipu() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_ipu();
    }
    return device_opt_.has_value() && device_opt_->type() == kIPU;
  }

  bool is_xla() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_xla();
    }
    return device_opt_.has_value() && device_opt_->type() == kXLA;
  }

  bool is_mtia() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_mtia();
    }
    return device_opt_.has_value() && device_opt_->type() == kMTIA;
  }

  bool is_hpu() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_hpu();
    }
    return device_opt_.has_value() && device_opt_->type() == kHPU;
  }

  bool is_lazy() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_lazy();
    }
    return device_opt_.has_value() && device_opt_->type() == kLazy;
  }

  bool is_hip() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_hip();
    }
    return device_opt_.has_value() && device_opt_->type() == kHIP;
  }

  bool is_ve() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_ve();
    }
    return device_opt_.has_value() && device_opt_->type() == kVE;
  }

  bool is_privateuseone() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_privateuseone();
    }
    return device_opt_.has_value() && device_opt_->type() == kPrivateUse1;
  }

  bool is_mkldnn() const {
    return key_set_.has_all(c10::mkldnn_ks);
  }

  bool is_vulkan() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_vulkan();
    }
    return device_opt_.has_value() && device_opt_->type() == kVulkan;
  }

  bool is_metal() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_metal();
    }
    return device_opt_.has_value() && device_opt_->type() == kMetal;
  }

  bool is_mps() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_mps();
    }
    return device_opt_.has_value() && device_opt_->type() == kMPS;
  }

  bool is_maia() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_maia();
    }
    return device_opt_.has_value() && device_opt_->type() == kMAIA;
  }

  bool is_nested() const {
    return key_set_.has(DispatchKey::NestedTensor);
  }

  // TODO: remove this once we don't automatically enabled Autograd dispatch
  // keys
  //       in TensorImpl constructor.
  // DON'T USE THIS API!! It's only created for testing purpose in
  // file aten/src/ATen/core/boxing/impl/test_helpers.h
  void remove_autograd_key() {
    key_set_ = key_set_ - autograd_dispatch_keyset;
  }

  // Inference tensor doesn't have autograd or ADInplaceOrView key.
  // Invariant:
  //   Inference tensor has version_counter_.enabled() == false
  bool is_inference() {
    bool no_ADInplaceOrView = !key_set_.has_any(c10::inplace_or_view_ks);
    bool no_Autograd = !key_set_.has_any(c10::autograd_dispatch_keyset);
    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
        no_ADInplaceOrView == no_Autograd,
        "ADInplaceOrView and Autograd keys must be on/off at the same time.");
    return no_ADInplaceOrView && no_Autograd;
  }

  DeviceIndex get_device() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().index();
    }
    return device_default().index();
  }

  Device device() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom();
    }
    return device_default();
  }

 protected:
  c10::Device device_default() const {
    TORCH_CHECK(device_opt_.has_value(), "tensor does not have a device");
    // See NOTE [std::optional operator usage in CUDA]
    return *device_opt_;
  }

 public:
  Layout layout() const {
    if (C10_UNLIKELY(layout_policy_)) {
      return layout_custom();
    }

    // NB: This method is not virtual and avoid dispatches for perf.
    // strided is also the most common layout type, so we check for
    // strided case first.
    // This keyset must also be kept in sync with the logic in
    // is_sparse() / is_sparse_csr() / is_mkldnn()
    constexpr auto sparse_and_sparsecsr_and_mkldnn_ks =
        c10::sparse_ks | c10::sparse_csr_ks | c10::mkldnn_ks;
    if (!key_set_.has_any(sparse_and_sparsecsr_and_mkldnn_ks)) {
      return kStrided;
    } else if (is_sparse()) {
      return kSparse;
    } else if (is_sparse_compressed()) {
      // Typically, the tensor dispatch keys define the tensor layout
      // uniquely. This allows using non-virtual layout method for
      // better performance. However, when tensor's layout depends,
      // say, on tensor attributes, one must use this execution path
      // where the corresponding tensor impl class overwrites virtual
      // layout_impl() method.
      //
      // TODO: implement layout() as native function/method so that
      // __torch_dispatch__ users will be able to redefine the
      // layout() method.
      return layout_impl();
    } else {
      TORCH_INTERNAL_ASSERT(
          is_mkldnn(), "There is an error in the layout calculation logic.");
      return kMkldnn;
    }
  }

  /**
   * True if a tensor was auto-wrapped from a C++ or Python number.
   * For example, when you write 't + 2', 2 is auto-wrapped into a Tensor
   * with `is_wrapped_number_` set to true.
   *
   * Wrapped numbers do not participate in the result type computation for
   * mixed-type operations if there are any Tensors that are not wrapped
   * numbers.  This is useful, because we want 't + 2' to work with
   * any type of tensor, not just LongTensor (which is what integers
   * in Python represent).
   *
   * Otherwise, they behave like their non-wrapped equivalents.
   * See [Result type computation] in TensorIterator.h.
   *
   * Why did we opt for wrapped numbers, as opposed to just having
   * an extra function add(Tensor, Scalar)?  This helps greatly reduce
   * the amount of code we have to write for add, when actually
   * a Tensor-Scalar addition is really just a Tensor-Tensor
   * addition when the RHS is 0-dim (except for promotion behavior.)
   */
  bool is_wrapped_number() const {
    return is_wrapped_number_;
  }

  /**
   * Set whether or not a tensor was auto-wrapped from a C++ or Python
   * number.  You probably don't want to call this, unless you are
   * writing binding code.
   */
  void set_wrapped_number(bool value) {
    TORCH_INTERNAL_ASSERT(dim() == 0);
    is_wrapped_number_ = value;
  }

  /**
   * Returns true if Tensor supports as_strided and as_strided_backward.
   * This is used in autograd to perform inplace update on view Tensors.
   * See Note [View + Inplace update for base tensor] and
   * [View + Inplace update for view tensor] for details.
   * Note this method only returns true for XLA backend, where it
   * simulates strided Tensor to support most view ops, but it cannot
   * fully support general `as_strided` case.
   * It can be expanded as needed in the future, e.g sparse Tensor.
   */
  inline bool support_as_strided() const {
    if (is_nested()) {
      return false;
    }
    if (key_set_.has(DispatchKey::Functionalize)) {
      return false;
    }
    return device().supports_as_strided();
  }

  // ~~~~~ Autograd API ~~~~~
  // Some methods below are defined in TensorImpl.cpp because Tensor is an
  // incomplete type.

  /**
   * Set whether or not a tensor requires gradient.
   */
  void set_requires_grad(bool requires_grad);

  /**
   * True if a tensor requires gradient.  Tensors which require gradient
   * have history tracked for any operations performed on them, so that
   * we can automatically differentiate back to them.  A tensor that
   * requires gradient and has no history is a "leaf" tensor, which we
   * accumulate gradients into.
   */
  bool requires_grad() const;

  /**
   * Return a mutable reference to the gradient.  This is conventionally
   * used as `t.grad() = x` to set a gradient to a completely new tensor.
   */
  at::Tensor& mutable_grad();

  /**
   * Return the accumulated gradient of a tensor.  This gradient is written
   * into when performing backwards, when this tensor is a leaf tensor.
   */
  const at::Tensor& grad() const;

  /**
   * Whether or not the imaginary part of the tensor should be negated
   */
  inline bool is_conj() const {
    constexpr auto conjugate_ks = DispatchKeySet(DispatchKey::Conjugate);
    return key_set_.has_all(conjugate_ks);
  }

  /**
   * Transmute this meta tensor into a fake tensor
   * The underlying device_opt_ stays as Meta for dispatch routing
   * and fake device is stored in ExtraMeta and returned by device()
   * via the device_policy_ mechanism
   * also converting backend key from Meta to Fake and adding Fake key
   * to DispatchKeySet
   */

  // this is the fast path: caller guarantees fake_device already has a valid
  // index
  void set_fake_device(c10::Device fake_device);

  // Normalizes the device index then calls set_fake_device.
  // use when the device might lack an index ("cuda" vs "cuda:0").
  void set_and_normalize_fake_device(c10::Device fake_device);

  void set_fake_tensor_mode(std::shared_ptr<FakeTensorMode> mode) {
    get_extra_meta().fake_tensor_mode_ = std::move(mode);
  }

  std::shared_ptr<FakeTensorMode> fake_tensor_mode() const {
    if (!extra_meta_) {
      return nullptr;
    }
    return extra_meta_->fake_tensor_mode_;
  }

  /**
   * Set whether or not to take the conjugate of the tensor (flip the imaginary
   * bit).
   */
  void _set_conj(bool value) {
    if (value) {
      key_set_ = key_set_.add(DispatchKey::Conjugate);
      TORCH_INTERNAL_ASSERT(isComplexType(typeMetaToScalarType(dtype())));
    } else {
      key_set_ = key_set_.remove(DispatchKey::Conjugate);
    }
  }

  /**
   * XXX: do not use, private api!
   * Update the backend component related keys to the backend component
   * corresponding to this device.
   */
  void _change_backend_component_keys(c10::Device device);

  /**
   * Whether or not the tensor is a zerotensor
   */
  inline bool _is_zerotensor() const {
    constexpr auto zerotensor_ks = DispatchKeySet(DispatchKey::ZeroTensor);
    return key_set_.has_all(zerotensor_ks);
  }

  /**
   Set whether or not the tensor is a zero tensor
  */
  void _set_zero(bool value) {
    if (value) {
      TORCH_INTERNAL_ASSERT(
          false,
          "Please call `torch._efficientzerotensor` if you want to create a tensor with no storage.");
    } else {
      key_set_ = key_set_.remove(DispatchKey::ZeroTensor);
    }
  }

  /**
   * Whether or not the tensor should be negated
   */
  inline bool is_neg() const {
    constexpr auto negative_ks = DispatchKeySet(DispatchKey::Negative);
    return key_set_.has_all(negative_ks);
  }

  /**
   * Set whether or not to take the conjugate of the tensor (flip the imaginary
   * bit).
   */
  void _set_neg(bool value) {
    if (value) {
      key_set_ = key_set_.add(DispatchKey::Negative);
    } else {
      key_set_ = key_set_.remove(DispatchKey::Negative);
    }
  }

  /**
   * Return the accumulated gradient of a tensor. This gradient is computed
   * using forward mode AD.
   *
   * This is an internal API that should never be used by end users.
   *
   * The API is as follows:
   *   - "level" allows to specify the level of forward AD nesting for which the
   *     gradient should be returned. Note that since levels are not fully
   *     supported yet, this argument should be 0. See documentation for
   *     torch::autograd::enter_dual_level for more details about forward AD
   * nesting.
   *   - "self" should represent the Tensor whose forward grad is accessed. It
   * is required when dealing with view.
   */
  const at::Tensor& _fw_grad(uint64_t level, const at::TensorBase& self) const;

  /**
   * Sets the forward gradient for this Tensor.
   * The given Tensor might not be used directly and its content will be copied.
   *
   * This is an internal API that should never be used by end users.
   *
   * The API is as follows:
   *   - "new_grad" is a Tensor containing the new value of the gradient that
   * should be set
   *   - "self" should represent the Tensor whose forward grad is accessed. It
   * is required when dealing with view.
   *   - "level" allows to specify the level of forward AD nesting for which the
   *     gradient should be set. Note that since levels are not fully supported
   *     yet, this argument should be 0. See documentation for
   * torch::autograd::enter_dual_level for more details about forward AD
   * nesting.
   *   - "is_inplace_op" is a boolean flag that tells if this gradient was
   * generated by an inplace operation or an out of place one. This allows
   * better error checking.
   */
  void _set_fw_grad(
      const at::TensorBase& new_grad,
      const at::TensorBase& self,
      uint64_t level,
      bool is_inplace_op);

  /**
   * Return a typed data pointer to the actual data which this tensor refers to.
   * This checks that the requested type (from the template parameter) matches
   * the internal type of the tensor.
   *
   * It is invalid to call data() on a dtype-uninitialized tensor, even if
   * the size is 0.
   *
   * WARNING: If a tensor is not contiguous, you MUST use strides when
   * performing index calculations to determine the location of elements in
   * the tensor.  We recommend using 'TensorAccessor' to handle this computation
   * for you; this class is available from 'Tensor'.
   */
  template <typename T>
  const T* data_dtype_initialized() const {
    return data_dtype_initialized_impl<const T>(
        [this] { return static_cast<const T*>(storage_.data()); });
  }

  /**
   * Return a mutable typed data pointer to the actual data which this
   * tensor refers to. This checks that the requested type (from the
   * template parameter) matches the internal type of the tensor.
   *
   * It is invalid to call data() on a dtype-uninitialized tensor, even if
   * the size is 0.
   *
   * WARNING: If a tensor is not contiguous, you MUST use strides when
   * performing index calculations to determine the location of elements in
   * the tensor.  We recommend using 'TensorAccessor' to handle this computation
   * for you; this class is available from 'Tensor'.
   */
  template <typename T>
  T* mutable_data_dtype_initialized() {
    return data_dtype_initialized_impl<T>(
        [this] { return static_cast<T*>(storage_.mutable_data()); });
  }

 private:
  // Shared implementation of data_dtype_initialized() and
  // mutable_data_dtype_initialized().
  template <typename T, typename Func>
  T* data_dtype_initialized_impl(const Func& get_data) const {
    TORCH_CHECK(
        data_type_.Match<std::remove_const_t<T>>(),
        "Tensor type mismatch, caller expects elements to be ",
        caffe2::TypeMeta::TypeName<std::remove_const_t<T>>(),
        ", while tensor contains ",
        data_type_.name(),
        ". ");
    return data_ptr_impl_impl<T>(get_data);
  }

 public:
  /**
   * More efficient helper for Tensor::data_ptr(). Like data<T>(), but
   * does not do a type check. Unlike the untemplated data(), does
   * check has_storage() and storage_initialized().
   */
  template <typename T>
  inline const T* data_ptr_impl() const {
    return data_ptr_impl_impl<const T>(
        [this] { return static_cast<const T*>(storage_.data()); });
  }

  /**
   * More efficient helper for Tensor::data_ptr(). Like data<T>(), but
   * does not do a type check. Unlike the untemplated data(), does
   * check has_storage() and storage_initialized().
   */
  template <typename T>
  inline T* mutable_data_ptr_impl() {
    return data_ptr_impl_impl<T>(
        [this] { return static_cast<T*>(storage_.mutable_data()); });
  }

 private:
  // Shared implementation of mutable_data_ptr_impl() and the future
  // mutable_data_ptr_impl().
  template <typename T, typename Func>
  __ubsan_ignore_pointer_overflow__ T* data_ptr_impl_impl(
      const Func& get_data) const {
    if (C10_UNLIKELY(!has_storage())) {
      throw_data_ptr_access_error();
    }
    TORCH_CHECK(
        storage_initialized(),
        "The tensor has a non-zero number of elements, but its data is not allocated yet.\n"
        "If you're using torch.compile/export/fx, it is likely that we are erroneously "
        "tracing into a custom kernel. To fix this, please wrap the custom kernel into "
        "an opaque custom op. Please see the following for details: "
        "https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html\n"
        "If you're using Caffe2, Caffe2 uses a lazy allocation, so you will need to call "
        "mutable_data() or raw_mutable_data() to actually allocate memory.");
    // Caller does the type check.
    // Note: storage_offset_ can be non-null even for zero-elements tensors
    // (for example if created as `torch.empty(5)[10:]`) that triggers
    // applying non-zero offset to null pointer in UBSan
    return get_data() + storage_offset_;
  }

 public:
  /**
   * Return a const void* data pointer to the actual data which this
   * tensor refers to.
   *
   * It is invalid to call data() on a dtype-uninitialized tensor, even if the
   * size is 0.
   *
   * WARNING: The data pointed to by this tensor may not contiguous; do NOT
   * assume that itemsize() * numel() is sufficient to compute the bytes that
   * can be validly read from this tensor.
   */
  inline const void* data() const {
    return data_impl<const void>(
        [this] { return static_cast<const char*>(storage_.data()); });
  }

  /**
   * Return a void* data pointer to the actual data which this tensor refers to.
   *
   * It is invalid to call mutable_data() on a dtype-uninitialized
   * tensor, even if the size is 0.
   *
   * WARNING: The data pointed to by this tensor may not contiguous; do NOT
   * assume that itemsize() * numel() is sufficient to compute the bytes that
   * can be validly read from this tensor.
   */
  inline void* mutable_data() {
    return data_impl<void>(
        [this] { return static_cast<char*>(storage_.mutable_data()); });
  }

 private:
  /// Shared implementation of data() and mutable_data().
  ///
  /// get_data must return a byte-addressed pointer, e.g. char*,
  /// std::byte const*, etc.
  template <typename Void, typename Func>
  Void* data_impl(const Func& get_data) const {
    if (C10_UNLIKELY(!has_storage())) {
      throw_data_ptr_access_error();
    }
    TORCH_CHECK(
        dtype_initialized(),
        "Cannot access data pointer of Tensor that doesn't have initialized dtype "
        "(e.g., caffe2::Tensor x(CPU), prior to calling mutable_data<T>() on x)");
    auto* data = get_data();
    static_assert(
        sizeof(*data) == 1, "get_data must return a byte-addressed pointer.");
    // Computing an offset into an empty tensor would be UB, since an empty
    // tensor's storage will be nullptr, and adding a nonzero offset to nullptr
    // is UB.  So we skip the offset computation in this case.
    if (is_empty()) {
      return nullptr;
    }
    return data + data_type_.itemsize() * storage_offset_;
  }

 public:
  /**
   * Returns the TypeMeta of a tensor, which describes what data type
   * it is (e.g., int, float, ...)
   */
  const caffe2::TypeMeta dtype() const {
    return data_type_;
  }

  /**
   * Return the size of a single element of this tensor in bytes.
   */
  size_t itemsize() const {
    TORCH_CHECK(
        dtype_initialized(),
        "Cannot report itemsize of Tensor that doesn't have initialized dtype "
        "(e.g., caffe2::Tensor x(CPU), prior to calling mutable_data<T>() on x)");
    return data_type_.itemsize();
  }

  void set_backend_meta(intrusive_ptr<c10::BackendMeta> backend_meta) {
    get_extra_meta().backend_meta_ = std::move(backend_meta);
  }

  c10::BackendMeta* get_backend_meta() {
    if (!extra_meta_) {
      return nullptr;
    }
    return extra_meta_->backend_meta_.get();
  }

  intrusive_ptr<c10::BackendMeta> get_backend_meta_intrusive_ptr() const {
    if (!extra_meta_) {
      return nullptr;
    }
    return extra_meta_->backend_meta_;
  }

  void release_storage_and_set_meta_custom_data_ptr_error_msg_(
      std::optional<std::string> s) {
    storage_ = {};
    set_storage_access_should_throw();
    get_extra_meta().custom_data_ptr_error_msg_ = s;
    get_extra_meta().custom_storage_error_msg_ = std::move(s);
  }

 protected:
  /**
   * Returns the human-readable name of the actual type of this object (e.g.,
   * TensorImpl, BatchedTensorImpl, etc.). Used for error messages.
   */
  virtual const char* tensorimpl_type_name() const {
    return "TensorImpl";
  }

 private:
  [[noreturn]] void throw_storage_access_error() const;
  [[noreturn]] void throw_data_ptr_access_error() const;

  ExtraMeta& get_extra_meta() {
    if (!extra_meta_) {
      extra_meta_ = std::make_unique<ExtraMeta>();
    }
    return *extra_meta_;
  }

  c10::SymbolicShapeMeta& symbolic_shape_meta() {
    TORCH_INTERNAL_ASSERT(extra_meta_ && extra_meta_->symbolic_shape_meta_);
    return *extra_meta_->symbolic_shape_meta_;
  }

  const c10::SymbolicShapeMeta& symbolic_shape_meta() const {
    TORCH_INTERNAL_ASSERT(extra_meta_ && extra_meta_->symbolic_shape_meta_);
    return *extra_meta_->symbolic_shape_meta_;
  }

 public:
  /**
   * True if a tensor has no elements (e.g., numel() == 0).
   */
  inline bool is_empty() const {
    return numel() == 0;
  }

  // if we are going to use sym sizes, we should be setting sym strides at the
  // same time, otherwise it's very easy to misuse this API
  void set_sizes_and_strides(
      c10::SymIntArrayRef sizes,
      c10::SymIntArrayRef strides,
      std::optional<c10::SymInt> storage_offset = std::nullopt);
  // This is renamed to avoid breaking overload BC
  void generic_set_sizes_contiguous(c10::SymIntArrayRef sizes);
  void generic_set_sizes_contiguous(c10::IntArrayRef sizes) {
    set_sizes_contiguous(sizes);
  }

  /**
   * Change the size at some dimension.  This DOES NOT update strides;
   * thus, most changes to size will not preserve contiguity.  You probably
   * also want to call set_stride() when you call this.
   *
   * TODO: This should be jettisoned in favor of `set_sizes_and_strides`,
   * which is harder to misuse.
   */
  virtual void set_size(int64_t dim, int64_t new_size) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_size ",
        err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(
        !matches_policy(SizesStridesPolicy::CustomSizes),
        "set_size() called on tensor with dynamic shapes or customized size behavior")
    sizes_and_strides_.size_at(dim) = new_size;
    refresh_numel();
    refresh_contiguous();
  }

  /**
   * Change the stride at some dimension.
   *
   * TODO: This should be jettisoned in favor of `set_sizes_and_strides`,
   * which is harder to misuse.
   */
  virtual void set_stride(int64_t dim, int64_t new_stride) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_stride ",
        err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "set_stride() called on tensor with symbolic shape")
    sizes_and_strides_.stride_at_unchecked(dim) = new_stride;
    refresh_contiguous();
  }

  /**
   * Set the offset into the storage of this tensor.
   *
   * WARNING: This does NOT check if the tensor is in bounds for the new
   * location at the storage; the caller is responsible for checking this
   * (and resizing if necessary.)
   */
  virtual void set_storage_offset(int64_t storage_offset) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_storage_offset ",
        err_msg_tensor_metadata_change_not_allowed);
    // TODO: this should probably consult policy
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "set_storage_offset() called on tensor with symbolic shape")
    storage_offset_ = storage_offset;
  }

  /**
   * Like set_sizes_and_strides but assumes contiguous strides.
   *
   * WARNING: This function does not check if the requested
   * sizes/strides are in bounds for the storage that is allocated;
   * this is the responsibility of the caller
   */
  void set_sizes_contiguous(IntArrayRef new_size) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_sizes_contiguous ",
        err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(
        !matches_policy(SizesStridesPolicy::CustomStrides),
        "tried to directly modify sizes for customized tensor");
    sizes_and_strides_.set_sizes(new_size);

    refresh_numel();
    empty_tensor_restride(
        MemoryFormat::Contiguous); // calls refresh_contiguous()
  }

  C10_ALWAYS_INLINE const impl::SizesAndStrides& sizes_and_strides() {
    return sizes_and_strides_;
  }

  /**
   * Set the sizes and strides of a tensor.
   *
   * WARNING: This function does not check if the requested
   * sizes/strides are in bounds for the storage that is allocated;
   * this is the responsibility of the caller
   */
  void set_sizes_and_strides(
      IntArrayRef new_size,
      IntArrayRef new_stride,
      std::optional<int64_t> storage_offset = std::nullopt) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_sizes_and_strides ",
        err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "set_sizes_and_strides() called on tensor with symbolic shape")
    TORCH_CHECK(
        new_size.size() == new_stride.size(),
        "dimensionality of sizes (",
        new_size.size(),
        ") must match dimensionality of strides (",
        new_stride.size(),
        ")");
    const auto new_dim = new_size.size();
    bool overflowed = false;
    sizes_and_strides_.set_sizes(new_size);

    if (new_dim > 0) {
      for (size_t dim = new_dim - 1;; dim--) {
        if (new_stride[dim] >= 0) {
          sizes_and_strides_.stride_at_unchecked(dim) = new_stride[dim];
        } else {
          // XXX: This behavior is surprising and may need to be removed to
          // support negative strides. Some pytorch functions rely on it:
          // for example, torch.cat (run TestTorch.test_cat_empty).
          if (dim == new_dim - 1) {
            sizes_and_strides_.stride_at_unchecked(dim) = 1;
          } else {
            // Keep stride monotonically increasing to match NumPy.
            overflowed |= c10::mul_overflows(
                sizes_and_strides_.stride_at_unchecked(dim + 1),
                std::max<int64_t>(
                    sizes_and_strides_.size_at_unchecked(dim + 1), 1),
                std::addressof(sizes_and_strides_.stride_at_unchecked(dim)));
          }
        }
        if (dim == 0)
          break;
      }
      TORCH_CHECK(!overflowed, "Stride calculation overflowed");
    }

    refresh_numel();
    refresh_contiguous();

    if (storage_offset.has_value()) {
      storage_offset_ = *storage_offset;
    }
  }

  /**
   * Set whether a tensor allows changes to its metadata (e.g. sizes / strides /
   * storage / storage_offset). See NOTE [ Metadata Change for a Detached Tensor
   * ] for details.
   */
  void set_allow_tensor_metadata_change(bool value [[maybe_unused]]) {
    // TODO: at some point, we should kill this field completely.
    allow_tensor_metadata_change_ = true;
  }

  /**
   * True if a tensor allows changes to its metadata (e.g. sizes / strides /
   * storage / storage_offset). See NOTE [ Metadata Change for a Detached Tensor
   * ] for details.
   */
  bool allow_tensor_metadata_change() const {
    return allow_tensor_metadata_change_;
  }

  /**
   * Set the pointer to autograd metadata.
   */
  void set_autograd_meta(
      std::unique_ptr<c10::AutogradMetaInterface> autograd_meta);

  /**
   * Return the pointer to autograd metadata.  May return nullptr if the
   * tensor does not track gradients.
   */
  c10::AutogradMetaInterface* autograd_meta() const;

  void set_python_dispatch(bool k) {
    if (k) {
      key_set_ = key_set_.add(c10::python_ks);
    } else {
      key_set_ = key_set_ - c10::python_ks;
    }
  }

  bool is_python_dispatch() const {
    return key_set_.has_all(c10::python_ks);
  }

  // NOTE [ TensorImpl Shallow-Copying ]
  //
  // TensorImpl shallow-copying is used when we want to have two Variables share
  // the same tensor metadata (e.g. sizes / strides / storage pointer /
  // storage_offset), but each with a different autograd history. Example call
  // sites:
  //
  // 1. `var_detached = var.detach()` uses `shallow_copy_and_detach()` to create
  // `var_detached` that shares the same tensor metadata with `var`, but with a
  // completely new autograd history.
  // 2. `var.set_data(tensor)` uses `shallow_copy_from()` to copy tensor
  // metadata from `tensor` into `var`, while keeping `var`'s original
  // AutogradMeta.
  //
  // Functions that shallow-copy a TensorImpl (such as
  // `shallow_copy_and_detach()` / `shallow_copy_from()` /
  // `copy_tensor_metadata()`) copy the tensor metadata fields (e.g. sizes /
  // strides / storage pointer / storage_offset) by value. However, the
  // following fields are not copied:
  //
  // 1. the AutogradMeta pointer, because it is unique for each Variable.
  // 2. the version counter, because the destination TensorImpl's version
  // counter is either set to the passed-in `version_counter` (in
  // `shallow_copy_and_detach()` and `copy_tensor_metadata()`), or it is kept
  // intact (in `shallow_copy_from()`). See NOTE [ Version Counter Sharing ] for
  // details.
  //
  // In `shallow_copy_and_detach()` and `copy_tensor_metadata()`, the passed-in
  // `allow_tensor_metadata_change` determines whether the TensorImpl
  // shallow-copy allows changes to its metadata (e.g. sizes / strides / storage
  // / storage_offset). See NOTE [ Metadata Change for a Detached Tensor ] for
  // details.
  //
  // In `shallow_copy_from()`, we don't check the destination TensorImpl's
  // `allow_tensor_metadata_change_`, because `shallow_copy_from()` is used for
  // implementing functions such as `var.set_data(tensor)`, which changes
  // `var`'s tensor metadata and expects its `allow_tensor_metadata_change_` to
  // be ignored.

  /**
   * One TensorImpl can be copied to another TensorImpl if
   * - They have the same DispatchKeySet.
   * - Or both have DispatchKey::Dense and a supported dense backend
   *   (CPU, CUDA, MPS, HIP, XPU, HPU, MTIA)
   * - Or both have DispatchKey::Sparse and a supported sparse backend
   *   (CPU, CUDA, MPS, HIP, XPU)
   * - Or both have DispatchKey::SparseCsr
   * For PrivateUse1 backend, user can implement their own
   * `_has_compatible_shallow_copy_type` operator.
   * See OpenRegMinimal.cpp for an example of overriding this operator.
   */
  inline bool has_compatible_shallow_copy_type(DispatchKeySet from) {
    auto is_dense = [](DispatchKeySet ts) {
      constexpr auto dense_backends = DispatchKeySet(
          {BackendComponent::CPUBit,
           BackendComponent::CUDABit,
           BackendComponent::MPSBit,
           BackendComponent::HIPBit,
           BackendComponent::XPUBit,
           BackendComponent::HPUBit,
           BackendComponent::MTIABit});
      constexpr auto dense_k = DispatchKeySet(DispatchKey::Dense);
      return ts.has_any(dense_k) && ts.has_any(dense_backends);
    };
    auto is_sparse = [](DispatchKeySet ts) {
      constexpr auto sparse_backends = DispatchKeySet(
          {BackendComponent::CPUBit,
           BackendComponent::CUDABit,
           BackendComponent::MPSBit,
           BackendComponent::HIPBit,
           BackendComponent::XPUBit});
      constexpr auto sparse_k = DispatchKeySet(DispatchKey::Sparse);
      return ts.has_any(sparse_k) && ts.has_any(sparse_backends);
    };
    auto is_sparse_compressed = [](DispatchKeySet ts) {
      constexpr auto sparse_compressed_k =
          DispatchKeySet(DispatchKey::SparseCsr);
      return ts.has_any(sparse_compressed_k);
    };
    return (key_set_ == from) || (is_dense(key_set_) && is_dense(from)) ||
        (is_sparse(key_set_) && is_sparse(from)) ||
        (is_sparse_compressed(key_set_) && is_sparse_compressed(from));
    ;
  }

 private:
  template <typename VariableVersion>
  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach_core(
      VariableVersion&& version_counter,
      bool allow_tensor_metadata_change) const;

 public:
  /**
   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`,
   * see NOTE [ TensorImpl Shallow-Copying ].
   */
  virtual c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
      const c10::VariableVersion& version_counter,
      bool allow_tensor_metadata_change) const;

  /**
   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`,
   * see NOTE [ TensorImpl Shallow-Copying ].
   */
  virtual c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
      c10::VariableVersion&& version_counter,
      bool allow_tensor_metadata_change) const;

  /**
   * Shallow-copies data from another TensorImpl into this TensorImpl.
   *
   * For why this function doesn't check this TensorImpl's
   * `allow_tensor_metadata_change_`, see NOTE [ TensorImpl Shallow-Copying ].
   */
  virtual void shallow_copy_from(const c10::intrusive_ptr<TensorImpl>& impl) {
    copy_tensor_metadata(
        /*src_impl=*/impl.get(),
        /*dest_impl=*/this,
        /*version_counter=*/version_counter(),
        /*allow_tensor_metadata_change=*/allow_tensor_metadata_change());
  }

  // Inference tensor doesn't have version counter,
  // set_version_counter is no-op for them.
  void set_version_counter(const c10::VariableVersion& version_counter) {
    TORCH_CHECK(
        !(is_inference() && version_counter.enabled()),
        "Cannot set version_counter for inference tensor");
    version_counter_ = version_counter;
  }

  void set_version_counter(c10::VariableVersion&& version_counter) {
    TORCH_CHECK(
        !(is_inference() && version_counter.enabled()),
        "Cannot set version_counter for inference tensor");
    version_counter_ = std::move(version_counter);
  }

  const c10::VariableVersion& version_counter() const noexcept {
    return version_counter_;
  }

  void bump_version() {
    version_counter_.bump();
  }

  impl::PyObjectSlot* pyobj_slot() {
    return &pyobj_slot_;
  }

  const impl::PyObjectSlot* pyobj_slot() const {
    return &pyobj_slot_;
  }

  void incref_pyobject() const noexcept final;
  void decref_pyobject() const noexcept final;
  bool try_incref_pyobject() const noexcept final;

 private:
  // See NOTE [std::optional operator usage in CUDA]
  // We probably don't want to expose this publicly until
  // the note is addressed.
  std::optional<c10::Device> device_opt() const {
    return device_opt_;
  }

 public:
  /**
   * The device type of a Tensor, e.g., DeviceType::CPU or DeviceType::CUDA.
   */
  DeviceType device_type() const {
    // TODO: A useful internal assert would be to show that device_opt_ is null
    // only if you are an undefined tensor
    TORCH_CHECK(
        device_opt_.has_value(),
        "device_type cannot be run on undefined Tensor");
    // See NOTE [std::optional operator usage in CUDA]
    return (*device_opt_).type();
  }

  /**
   * @brief Extends the outer-most dimension of this tensor by num elements,
   * preserving the existing data.
   *
   * The underlying data may be reallocated in order to accommodate the new
   * elements, in which case this tensors' capacity is grown at a factor of
   * growthPct. This ensures that Extend runs on an amortized O(1) time
   * complexity.
   *
   * This op is auto-asynchronous if the underlying device (CUDA) supports it.
   */
  void Extend(int64_t num, float growthPct);

  /**
   * @brief Reserve space for the underlying tensor.
   *
   * This must be called after Resize(), since we only specify the first
   * dimension This does not copy over the old data to the newly allocated space
   */
  void ReserveSpace(int64_t outer_dim);

  /**
   * @brief Resizes a tensor.
   *
   * Resize takes in a vector of ints specifying the dimensions of the tensor.
   * You can pass in an empty vector to specify that it is a scalar (i.e.
   * containing one single item).
   *
   * The underlying storage may be deleted after calling Resize: if the new
   * shape leads to a different number of items in the tensor, the old memory
   * is deleted and new memory will be allocated next time you call
   * mutable_data(). However, if the shape is different but the total number of
   * items is the same, the underlying storage is kept.
   *
   * This method respects caffe2_keep_on_shrink.  Consult the internal logic
   * of this method to see exactly under what circumstances this flag matters.
   */
  template <typename... Ts>
  void Resize(Ts... dim_source) {
    bool size_changed = SetDims(dim_source...);
    if (size_changed) {
      HandleResize();
    }
  }

  template <typename T>
  void Resize(const std::vector<T>& dim_source) {
    Resize(ArrayRef<T>(dim_source));
  }

  /**
   * Resizes the tensor without touching underlying storage.
   * This requires the total size of the tensor to remains constant.
   */
  void Reshape(const std::vector<int64_t>& dims);

  /**
   * Release whatever memory the tensor was holding but keep size and type
   * information. Subsequent call to mutable_data will trigger new memory
   * allocation.
   */
  void FreeMemory();

  /**
   * @brief Shares the data with another tensor.
   *
   * To share data between two tensors, the sizes of the two tensors must be
   * equal already. The reason we do not implicitly do a Resize to make the two
   * tensors have the same shape is that we want to allow tensors of different
   * shapes but the same number of items to still be able to share data. This
   * allows one to e.g. have a n-dimensional Tensor and a flattened version
   * sharing the same underlying storage.
   *
   * The source tensor should already have its data allocated.
   */
  // To be deprecated
  void ShareData(const TensorImpl& src);

  void ShareExternalPointer(
      DataPtr&& data_ptr,
      const caffe2::TypeMeta data_type,
      size_t size_bytes);

  /**
   * Returns a mutable raw pointer of the underlying storage. Since we will need
   * to know the type of the data for allocation, a TypeMeta object is passed in
   * to specify the necessary information. This is conceptually equivalent of
   * calling mutable_data<T>() where the TypeMeta parameter meta is derived from
   * the type T. This function differs from mutable_data<T>() in the sense that
   * the type T can be specified during runtime via the TypeMeta object.
   *
   * If the existing data does not match the desired type, it will be deleted
   * and a new storage will be created.
   */
  inline void* raw_mutable_data(const caffe2::TypeMeta& meta) {
    // For 0-size tensors it's fine to return any pointer (including nullptr)
    if (data_type_ == meta && storage_initialized()) {
      return static_cast<void*>(
          static_cast<char*>(storage_.mutable_data()) +
          storage_offset_ * meta.itemsize());
    } else {
      bool had_special_dtor = data_type_.placementDelete() != nullptr;
      storage_offset_ = 0;
      data_type_ = meta;
      // NB: device is not changed

      // We can reuse the existing buffer if the current data does not have
      // a special destructor and the new data doesn't have a special
      // constructor.
      if (numel_ == 0 ||
          (meta.placementNew() == nullptr && !had_special_dtor &&
           (storage_.nbytes() >= (numel_ * data_type_.itemsize())))) {
        TORCH_INTERNAL_ASSERT(
            storage_offset_ == 0); // because we just reallocated
        return storage_.mutable_data();
      }
      Allocator* allocator = storage_.allocator();
      // Storage might have nullptr allocator in rare cases, for example, if
      // an external memory segment has been wrapped with Tensor and we don't
      // know how to reallocate it. However, in order to preserve legacy C2
      // behavior, we allow reallocating the memory using default allocator.
      if (allocator == nullptr) {
        allocator = GetAllocator(storage_.device_type());
      }
      if (meta.placementNew()) {
        // For types that need placement new, we will call it, as well as
        // making sure that when the data is freed, it calls the right
        // destruction procedure.
        auto size = numel_;
        auto dtor = data_type_.placementDelete();
        auto data_ptr = allocator->allocate(numel_ * data_type_.itemsize());
        storage_.set_data_ptr_noswap(PlacementDeleteContext::makeDataPtr(
            std::move(data_ptr), dtor, size, storage_.device()));
        data_type_.placementNew()(storage_.mutable_data(), numel_);
      } else {
        // For fundamental type, new and delete is easier.
        storage_.set_data_ptr_noswap(
            allocator->allocate(numel_ * data_type_.itemsize()));
      }
      storage_.set_nbytes(numel_ * data_type_.itemsize());
      TORCH_INTERNAL_ASSERT(
          storage_offset_ == 0); // because we just reallocated
      device_opt_ = storage_.device();
      return storage_.mutable_data();
    }
  }

  /**
   * Returns a typed pointer of the underlying storage.
   *
   * For fundamental types, we reuse possible existing storage if there
   * is sufficient capacity.
   */
  template <typename T>
  inline T* mutable_data() {
    if (storage_initialized() && data_type_.Match<T>()) {
      return static_cast<T*>(storage_.mutable_data()) + storage_offset_;
    }
    // Check it here statically - otherwise TypeMeta would throw the runtime
    // error in attempt to invoke TypeMeta::ctor()
    static_assert(
        std::is_default_constructible_v<T>,
        "Tensor can't hold non-default-constructable types");
    return static_cast<T*>(raw_mutable_data(caffe2::TypeMeta::Make<T>()));
  }

  /**
   * True if a tensor is storage initialized.  A tensor may become
   * storage UNINITIALIZED after a Resize() or FreeMemory()
   */
  bool storage_initialized() const {
    TORCH_CHECK(
        has_storage(),
        "cannot call storage_initialized on tensor that does not have storage");
    return storage_.data() || numel_ == 0;
  }

  /**
   * True if a tensor is dtype initialized.  A tensor allocated with
   * Caffe2-style constructors is dtype uninitialized until the
   * first time mutable_data<T>() is called.
   */
  bool dtype_initialized() const noexcept {
    return data_type_ != caffe2::TypeMeta();
  }

  void set_storage_keep_dtype(at::Storage storage) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_storage ",
        err_msg_tensor_metadata_change_not_allowed);
    storage_ = std::move(storage);
    device_opt_ = storage_.device();
  }

  void set_storage_and_dtype(
      at::Storage storage,
      const caffe2::TypeMeta data_type) {
    set_storage_keep_dtype(std::move(storage));
    data_type_ = data_type;
  }

  void empty_tensor_restride_symint(MemoryFormat memory_format);

  /**
   * Set the strides of the tensor to match memory_format
   *
   * WARNING: This function doesn't rearrange data and assumes tensor is a
   * memory contiguous
   */
  void empty_tensor_restride(MemoryFormat memory_format) {
    if (has_symbolic_sizes_strides_) {
      empty_tensor_restride_symint(memory_format);
      return;
    }
#ifdef DEBUG
    TORCH_INTERNAL_ASSERT(
        compute_numel() == numel_,
        "If you are seeing this error, that means empty_tensor_restride was "
        "called before setting correct numel");
#endif
    switch (memory_format) {
      case MemoryFormat::Contiguous: {
        // dim_ is a virtual call, don't repeat it
        const auto dim_ = dim();
        sizes_and_strides_.resize(dim_);
        if (dim_ > 0) {
          bool overflowed = false;
          const auto last_idx = dim_ - 1;
          sizes_and_strides_.stride_at_unchecked(last_idx) = 1;
          for (auto i = last_idx - 1; i >= 0; --i) {
            overflowed |= c10::mul_overflows(
                sizes_and_strides_.stride_at_unchecked(i + 1),
                std::max<int64_t>(
                    sizes_and_strides_.size_at_unchecked(i + 1), 1),
                std::addressof(sizes_and_strides_.stride_at_unchecked(i)));
          }
          TORCH_CHECK(!overflowed, "Stride calculation overflowed");
        }
        break;
      }
      case MemoryFormat::ChannelsLast: {
        TORCH_CHECK(
            dim() == 4, "required rank 4 tensor to use channels_last format");
        set_sizes_and_strides(sizes(), get_channels_last_strides_2d(sizes()));
        break;
      }
      case MemoryFormat::ChannelsLast3d: {
        TORCH_CHECK(
            dim() == 5,
            "required rank 5 tensor to use channels_last_3d format");
        set_sizes_and_strides(sizes(), get_channels_last_strides_3d(sizes()));
        break;
      }
      case MemoryFormat::Preserve:
        TORCH_CHECK(false, "unsupported memory format ", memory_format);
        // Cleaning warning messages, no need to break as TORCH_CHECK(false)
        // terminates flow.
        // break;
      case MemoryFormat::NumOptions:
        TORCH_INTERNAL_ASSERT(false, "invalid memory format ", memory_format);
    }
    // recompute contiguous flag, as currently NHWC/NCHW flags are not mutually
    // exclusive see #24090
    refresh_contiguous();
  }

  bool is_strides_like(at::MemoryFormat memory_format) const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return is_strides_like_custom(memory_format);
    }
    return is_strides_like_default(memory_format);
  }

  bool is_strides_like_channels_last() const {
    return is_strides_like(at::MemoryFormat::ChannelsLast);
  }

  bool is_strides_like_channels_last_3d() const {
    return is_strides_like(at::MemoryFormat::ChannelsLast3d);
  }

  bool is_non_overlapping_and_dense_or_false() const {
    return sym_is_non_overlapping_and_dense().guard_or_false(
        __FILE__, __LINE__);
  }

  bool is_non_overlapping_and_dense() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return is_non_overlapping_and_dense_custom();
    }
    return is_non_overlapping_and_dense_default();
  }

  SymBool sym_is_non_overlapping_and_dense() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return sym_is_non_overlapping_and_dense_custom();
    }
    return sym_is_non_overlapping_and_dense_default();
  }

  // if this returns true, then it is guaranteed that this tensor has symbolic
  // sizes/strides
  bool has_symbolic_sizes_strides() const {
    return has_symbolic_sizes_strides_;
  }

 private:
  void HandleResize();

  // The Caffe2 Resize() method supports being called both as Resize({2,2}) as
  // well as variadic with Resize(2, 2).  These overloads provide all of the
  // supported calling configurations, while being overloads (and not templates)
  // so that implicit conversions still work.
  //
  // SetDims on ArrayRef is internally implemented as a template, so we can
  // handle both ArrayRefs of different types (there are some uses of
  // Resize in Caffe2 which pass in int, not int64_t.)

  template <
      typename T,
      typename = typename std::enable_if_t< // NOLINT(modernize-use-constraints)
          std::is_integral_v<T>>>
  bool SetDimsTemplate(ArrayRef<T> src) {
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "SetDims() called on tensor with symbolic shape")

    auto old_numel = numel_;
    sizes_and_strides_.resize(src.size());
    int64_t new_numel = 1;
    for (const auto i : c10::irange(src.size())) {
      new_numel *= src[i];
      sizes_and_strides_.size_at_unchecked(i) = src[i];
    }
    numel_ = new_numel;
    empty_tensor_restride(MemoryFormat::Contiguous);
    return numel_ != old_numel;
  }

  bool SetDims(ArrayRef<int64_t> s) {
    return SetDimsTemplate(s);
  }

  bool SetDims(ArrayRef<int> s) {
    return SetDimsTemplate(s);
  }

  bool SetDims(ArrayRef<size_t> s) {
    return SetDimsTemplate(s);
  }

  bool SetDims() {
    return SetDims(IntArrayRef{});
  }

  bool SetDims(const int64_t d0) {
    return SetDims(IntArrayRef{d0});
  }

  bool SetDims(const int64_t d0, const int64_t d1) {
    return SetDims(IntArrayRef{d0, d1});
  }

  bool SetDims(const int64_t d0, const int64_t d1, const int64_t d2) {
    return SetDims(IntArrayRef{d0, d1, d2});
  }

  bool SetDims(
      const int64_t d0,
      const int64_t d1,
      const int64_t d2,
      const int64_t d3) {
    return SetDims(IntArrayRef{d0, d1, d2, d3});
  }

  /**
   * Compute the number of elements based on the sizes of a tensor.
   */
  // NB: This is ONLY called when sizes_and_strides_ is used directly; if
  // we are virtualizing, then numel calls are virtualized as well, and this
  // should never get called
  int64_t compute_numel() const {
    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!has_symbolic_sizes_strides_);
#if C10_HAS_BUILTIN_OVERFLOW() && !defined(C10_MOBILE)
    // Use overflow checks if supported by the compiler
    return safe_compute_numel();
#else
    return c10::multiply_integers(sizes_and_strides_.sizes_arrayref());
#endif
  }

  /**
   * Compute the number of elements based on the sizes of a
   * tensor. Catches integer overflow that may occur when a tensor
   * using a sparse layout has multiple dimensions with large sizes.
   */
  int64_t safe_compute_numel() const {
    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!has_symbolic_sizes_strides_);
    uint64_t n = 1;
    bool overflows =
        c10::safe_multiplies_u64(sizes_and_strides_.sizes_arrayref(), &n);
    constexpr auto numel_max = std::min(
        static_cast<uint64_t>(std::numeric_limits<int64_t>::max()),
        static_cast<uint64_t>(std::numeric_limits<size_t>::max()));

    overflows |= (n > numel_max);
    TORCH_CHECK(!overflows, "numel: integer multiplication overflow");
    return static_cast<int64_t>(n);
  }

  /**
   * Compute whether or not a tensor is contiguous based on the sizes and
   * strides of a tensor.
   */
  bool compute_contiguous() const;

  bool compute_channels_last_contiguous_2d() const;

  bool compute_channels_last_contiguous_3d() const;

  bool compute_strides_like_channels_last_2d() const;

  bool compute_strides_like_channels_last_3d() const;

  bool compute_non_overlapping_and_dense() const;

 protected:
  /**
   * Recompute the cached numel of a tensor.  Call this if you modify
   * sizes.
   *
   * For tensors with sparse layouts, use safe_refresh_numel() instead
   * because it will catch integer overflow that may occur for tensors
   * with sparse layouts and large dimensions.
   *
   * NB: We may uselessly recompute cached numel even in situations where
   * it is completely never used (e.g., if CustomSizes for Python).  However,
   * we still must keep it up to date in case the Python overload
   * returns None (in which case we will consult the field here).  This also
   * implies that sizes/strides will never be complete garbage; in the
   * very worst case scenario, it will reflect a 1-dim zero size tensor.
   */
  void refresh_numel() {
    if (has_symbolic_sizes_strides_) {
      symbolic_shape_meta().refresh_numel();
    } else {
      numel_ = compute_numel();
    }
  }

  /**
   * Recompute the cached numel of a tensor.  Call this if you modify
   * sizes. Use only for tensors with sparse layouts because only
   * sparse tensor are likely to have sizes that may lead to integer
   * overflow when computing numel.
   */
  void safe_refresh_numel() {
    if (has_symbolic_sizes_strides_) {
      // NB: sym numel is done with symbolic integers, which handle overflow
      // checking
      symbolic_shape_meta().refresh_numel();
    } else {
      numel_ = safe_compute_numel();
    }
  }

 private:
  void _set_is_contiguous(bool b) {
    is_contiguous_ = b;
  }

  void _set_is_channels_last_contiguous(bool b) {
    is_channels_last_contiguous_ = b;
  }

  void _set_is_channels_last_3d_contiguous(bool b) {
    is_channels_last_3d_contiguous_ = b;
  }

  void _set_is_channels_last(bool b) {
    is_channels_last_ = b;
  }

  void _set_is_channels_last_3d(bool b) {
    is_channels_last_3d_ = b;
  }

  void _set_is_non_overlapping_and_dense(bool b) {
    is_non_overlapping_and_dense_ = b;
  }

  // These are little wrappers over the real compute_ functions that
  // can make use of other contiguity fields to short circuit.

  bool compute_is_non_overlapping_and_dense_dim4() {
    return is_contiguous_ || is_channels_last_contiguous_ ||
        compute_non_overlapping_and_dense();
  }

  bool compute_channels_last_contiguous_3d_dim5() {
    return !is_channels_last_contiguous_ &&
        compute_channels_last_contiguous_3d();
  }

  bool compute_channels_last_2d_dim5() {
    return !is_channels_last_3d_contiguous_ &&
        compute_strides_like_channels_last_2d();
  }

  bool compute_channels_last_3d_dim5() {
    return !is_channels_last_ && compute_strides_like_channels_last_3d();
  }

  bool compute_is_non_overlapping_and_dense_dim5() {
    return is_contiguous_ || is_channels_last_contiguous_ ||
        is_channels_last_3d_contiguous_ || compute_non_overlapping_and_dense();
  }

  bool compute_is_non_overlapping_and_dense_anydim() {
    return is_contiguous_ || compute_non_overlapping_and_dense();
  }

  void _refresh_contiguous() {
    // Note:
    // Dim 0, 1, 2 will never be a channels last 2d/3d format
    // Dim 3+ is possibly be a channels last 2d format (Dim 4 only at this
    // point) Dim 4+ is possibly be a channels last 3d format (Dim 5 only at
    // this point)
    switch (dim()) {
      case 4: {
        _set_is_contiguous(compute_contiguous());
        _set_is_channels_last_contiguous(compute_channels_last_contiguous_2d());
        _set_is_channels_last_3d_contiguous(false);
        _set_is_channels_last(compute_strides_like_channels_last_2d());
        _set_is_channels_last_3d(false);
        _set_is_non_overlapping_and_dense(
            compute_is_non_overlapping_and_dense_dim4());
        break;
      }
      case 5: {
        _set_is_contiguous(compute_contiguous());
        _set_is_channels_last_contiguous(compute_channels_last_contiguous_2d());
        _set_is_channels_last_3d_contiguous(
            compute_channels_last_contiguous_3d_dim5());
        _set_is_channels_last(compute_channels_last_2d_dim5());
        _set_is_channels_last_3d(compute_channels_last_3d_dim5());
        _set_is_non_overlapping_and_dense(
            compute_is_non_overlapping_and_dense_dim5());
        break;
      }
      default:
        // is_channels_last_ and is_channels_last_3d_ are suggested
        // memory_format. Being channels_last_contiguous doesn't necessarily
        // mean the tensor is strided like channels_last: for strides on channel
        // dimension could suggest desired memory_layout, but it doesn't affect
        // memory storage
        _set_is_contiguous(compute_contiguous());
        _set_is_channels_last_contiguous(false);
        _set_is_channels_last_3d_contiguous(false);
        _set_is_channels_last(false);
        _set_is_channels_last_3d(false);
        _set_is_non_overlapping_and_dense(
            compute_is_non_overlapping_and_dense_anydim());
        break;
    }
  }

 protected:
  /**
   * Recompute the cached contiguity of a tensor.  Call this if you modify sizes
   * or strides.
   */
  void refresh_contiguous() {
    if (has_symbolic_sizes_strides_) {
      symbolic_shape_meta().refresh_contiguous();
    } else {
      _refresh_contiguous();
    }
  }

  /**
   * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer /
   * storage_offset) from one TensorImpl to another TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE
   * [ TensorImpl Shallow-Copying ].
   */
  static void copy_tensor_metadata(
      const TensorImpl* src_impl,
      TensorImpl* dest_impl,
      const c10::VariableVersion& version_counter,
      bool allow_tensor_metadata_change);

  /**
   * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer /
   * storage_offset) from one TensorImpl to another TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE
   * [ TensorImpl Shallow-Copying ].
   */
  static void copy_tensor_metadata(
      const TensorImpl* src_impl,
      TensorImpl* dest_impl,
      c10::VariableVersion&& version_counter,
      bool allow_tensor_metadata_change);

 private:
  static void copy_tensor_metadata_except_version_counter(
      const TensorImpl* src_impl,
      TensorImpl* dest_impl,
      bool allow_tensor_metadata_change);

 protected:
  // Error message to show when the user tries to change tensor metadata on
  // Tensor created from .data or .detach().
  //
  // See NOTE [ Metadata Change for a Detached Tensor ] for details.
  static const char* const err_msg_tensor_metadata_change_not_allowed;

  static void copy_generic_tensor_metadata(
      const TensorImpl* src_impl,
      TensorImpl* dest_impl);

 public:
  void set_storage_access_should_throw() {
    storage_access_should_throw_ = true;
  }

 public:
  void set_custom_sizes_strides(SizesStridesPolicy policy) {
    custom_sizes_strides_ = static_cast<uint8_t>(policy);
    refresh_sizes_strides_policy();
  }

  void set_python_custom_sizes_strides(SizesStridesPolicy policy) {
    python_custom_sizes_strides_ = static_cast<uint8_t>(policy);
    refresh_sizes_strides_policy();
  }

  void set_custom_device(bool custom_device) {
    custom_device_ = custom_device;
    refresh_device_policy();
  }

  void set_custom_layout(bool custom_layout) {
    custom_layout_ = custom_layout;
    refresh_layout_policy();
  }

  void set_python_custom_device(bool custom_device) {
    python_custom_device_ = custom_device;
    refresh_device_policy();
  }

  void set_python_custom_layout(bool custom_layout) {
    python_custom_layout_ = custom_layout;
    refresh_layout_policy();
  }

 protected:
  void refresh_sizes_strides_policy() {
    if (has_symbolic_sizes_strides_) {
      sizes_strides_policy_ =
          static_cast<uint8_t>(SizesStridesPolicy::CustomSizes);
    } else {
      sizes_strides_policy_ =
          std::max(custom_sizes_strides_, python_custom_sizes_strides_);
    }
  }

  void refresh_device_policy() {
    device_policy_ = custom_device_ || python_custom_device_;
  }

  void refresh_layout_policy() {
    layout_policy_ = custom_layout_ || python_custom_layout_;
  }

 protected:
  Storage storage_;

 private:
  // This pointer points to an AutogradMeta struct that stores autograd-specific
  // fields (such as grad_ / grad_fn_ / grad_accumulator_). This pointer always
  // has unique ownership (meaning only one TensorImpl can own it at a time).
  //
  // autograd_meta_ can be nullptr, as an optimization.  When this occurs, it is
  // equivalent to having an autograd_meta_ pointing to a default constructed
  // AutogradMeta; intuitively, tensors which don't require grad will have this
  // field set to null.
  //
  // This means accessors on autograd_meta_ have to be careful to test if they
  // got a nullptr, and handle default behavior appropriately in that case.
  //
  // Note that we don't enforce the invariant that if the AutogradMeta is
  // default constructed, it is nullptr (to do this, we'd have to continuously
  // check if an AutogradMeta became, by mutation, equal to the default
  // constructed form.  (This might be useful, but it seems rare enough that
  // a requires_grad=True variable will turn back into the requires_grad=False
  // version.)  So there are three representable states:
  //
  //    1. autograd_meta_ == nullptr
  //    2. autograd_meta_ is default constructed (semantically, same as (1))
  //    3. autograd_meta_ has nontrivial information content
  //
  std::unique_ptr<c10::AutogradMetaInterface> autograd_meta_ = nullptr;

 protected:
  std::unique_ptr<c10::ExtraMeta> extra_meta_ = nullptr;

  c10::VariableVersion version_counter_;

  impl::PyObjectSlot pyobj_slot_;

  c10::impl::SizesAndStrides sizes_and_strides_;

  int64_t storage_offset_ = 0;
  // If sizes and strides are empty, the numel is 1!!  However, most of the
  // time, we will immediately set sizes to {0} and reset numel to 0.
  // (Can't do that in the default initializers, because there's no way to
  // spell "allocate a one-element array" for strides_).
  int64_t numel_ = 1;

  // INVARIANT: When storage is non-null, this type meta must
  // agree with the type meta in storage
  caffe2::TypeMeta data_type_;

  // NOTE [std::optional operator usage in CUDA]
  // Our optional definition doesn't compile in .cu file if `value()` or
  // `operator->` are used.  Instead, we always use `operator*`.
  // See https://github.com/pytorch/pytorch/issues/18496 for more info.
  // If this is too burdensome to maintain, we can just
  // manually implement this with an additional bool.

  // INVARIANT: When storage is non-null, this Device must
  // agree with the type meta in storage.
  //
  // INVARIANT: device_opt_ is only nullopt for undefined tensors
  // (which do not have a device.)
  std::optional<c10::Device> device_opt_;

  // default member initializers for bit-fields only available with -std=c++2a
  // or -std=gnu++2a
  inline void init_bitfields() {
    is_contiguous_ = true;
    is_channels_last_ = false;
    is_channels_last_contiguous_ = false;
    is_channels_last_3d_ = false;
    is_channels_last_3d_contiguous_ = false;
    is_non_overlapping_and_dense_ = true;
    is_wrapped_number_ = false;
    allow_tensor_metadata_change_ = true;
    reserved_ = false;
    sizes_strides_policy_ = static_cast<uint8_t>(SizesStridesPolicy::Default);
    custom_sizes_strides_ = static_cast<uint8_t>(SizesStridesPolicy::Default);
    python_custom_sizes_strides_ =
        static_cast<uint8_t>(SizesStridesPolicy::Default);
    python_custom_device_ = false;
    python_custom_layout_ = false;
    custom_device_ = false;
    custom_layout_ = false;
    device_policy_ = false;
    layout_policy_ = false;
    storage_access_should_throw_ = false;
    has_symbolic_sizes_strides_ = false;
  }

  // Tensor is contiguous
  bool is_contiguous_ : 1;

  // Tensor is a subclass that does not permit storage access.
  bool storage_access_should_throw_ : 1;

  // Tensor is stored in the channels last 2d memory format, when dimensions
  // order is (N)CHW and C-strides < W-strides < H-strides (< N-strides)
  // (If size of any dimension is equal to 1, this dimension strides value
  // is not taken into account).
  bool is_channels_last_ : 1;

  // Channels last contiguous tensor is channel last tensor which occupies
  // contiguous memory block.
  bool is_channels_last_contiguous_ : 1;

  // Tensor is stored in the channels last 3d memory format, when dimensions
  // order is (N)CDHW and C-strides < W-strides < H-strides < D - strides (<
  // N-strides) (If size of any dimension is equal to 1, this dimension strides
  // value is not taken into account).
  bool is_channels_last_3d_ : 1;

  // Channels last 3d contiguous tensor is channel last 3d tensor which occupies
  // contiguous memory block.
  bool is_channels_last_3d_contiguous_ : 1;

  // Dense tensor is the tensor that store values in a contiguous block of
  // memory. Non-overlapping tensor is the tensor in which elements occupy
  // individual non-repetitive memory.
  bool is_non_overlapping_and_dense_ : 1;

  bool is_wrapped_number_ : 1;

  // NOTE [ Metadata Change for a Detached Tensor ]
  //
  // Normally, a user is allowed to change the tensor metadata
  // (e.g. sizes / strides / storage / storage_offset) of a tensor.
  // However, if the tensor is created by `t1_detached = t1.data` in Python
  // or `t1_detached = t1.detach()` in Python/C++, those changes to the
  // tensor metadata of `t1_detached` will not be propagated back to the
  // original tensor `t1`. In order to make such changes explicitly illegal,
  // we created the `allow_tensor_metadata_change_` flag, to prevent users
  // from changing metadata of the detached tensor and expecting the original
  // tensor to also be updated.
  //
  // NOTE: For a full list of tensor metadata fields, please see
  // `copy_tensor_metadata()` in TensorImpl and its subclasses to find
  // which fields are copied by value.
  bool allow_tensor_metadata_change_ : 1;

  // we decide to keep reserved_ and it will
  // live in Tensor after the split
  // The logic is that if Extend() or ReserveSpace() were ever called,
  // then subsequent Resize()s will not free up Storage.
  bool reserved_ : 1;

  // Call _custom() virtual methods for
  // strides()/is_contiguous()/sizes()/dim()/numel()
  // This is a combination of sizes_strides_custom_dispatch_
  // and has_symbolic_sizes_strides_
  uint8_t sizes_strides_policy_ : 2;

  // Whether or not sizes_and_strides_ contains a symbolic value.
  bool has_symbolic_sizes_strides_ : 1;

  // Call _custom() virtual method for
  // strides()/is_contiguous()/sizes()/dim()/numel()
  uint8_t custom_sizes_strides_ : 2;

  // Combo of custom_ and python_custom_
  bool device_policy_ : 1;
  bool layout_policy_ : 1;

  // Call _custom() virtual method for device()
  bool custom_device_ : 1;

  // Call _custom() virtual method for layout()
  bool custom_layout_ : 1;

  // Call into Python for
  // strides()/is_contiguous()/sizes()/dim()/numel()
  uint8_t python_custom_sizes_strides_ : 2;

  // Call into Python for device()
  bool python_custom_device_ : 1;

  // Call into Python for layout()
  bool python_custom_layout_ : 1;

  // The set of DispatchKeys which describe this tensor.  NB: this
  // does NOT include Autograd (historically, it did, but
  // not anymore!)
  DispatchKeySet key_set_;

 private:
  // C10_TensorImpl_Size_Check_Dummy_Class needs to be friends with
  // TensorImpl so it can inspect the size of private fields
  template <
      size_t cplusplus,
      size_t clang_ver_major,
      size_t gcc_ver,
      size_t gcc_ver_minor,
      size_t nvcc,
      size_t cuda_version,
      size_t cuda_version_major,
      size_t ptr_size>
  friend class C10_TensorImpl_Size_Check_Dummy_Class;
};

namespace detail {

#ifndef C10_MOBILE
template <class T>
struct TargetTraits<
    T,
    std::enable_if_t<std::is_base_of_v<c10::TensorImpl, std::remove_cv_t<T>>>> {
  static constexpr bool can_have_pyobject = true;
};
#endif

} // namespace detail

// Note [TensorImpl size constraints]
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// Changed the size of TensorImpl?  If the size went down, good for
// you!  Adjust the documentation below and the expected size.
// Did it go up?  Read on...
//
// Struct size matters.  In some production systems at Facebook, we have
// 400M live tensors during a training run.  Do the math: every 64-bit
// word you add to Tensor is an extra 3.2 gigabytes in RAM.
//
// If you are a Facebook employee, you can check if the run in question
// has tipped you over the point using the command here:
// https://fburl.com/q5enpv98
//
// For reference, we OOMed at 160 bytes (20 words) per TensorImpl.
// This is not counting overhead from strides out-of-line allocation and
// StorageImpl space and this is from before we inlined sizes and strides
// directly into TensorImpl as SmallVectors.
//
// Our memory usage on 32-bit systems is suboptimal, but we're not checking
// for it at the moment (to help avoid rage inducing cycles when the
// 32-bit number is wrong).
//
// Current breakdown:
//
//    vtable pointer
//    strong refcount           TODO: pack these into one word
//    weak refcount
//    storage pointer
//    autograd metadata pointer
//    version counter pointer
//    PyObjectSlot
//    SizesAndStrides size/pointer
//    SizesAndStrides sizes (pre-allocated 0)
//    SizesAndStrides sizes (pre-allocated 1)
//    SizesAndStrides sizes (pre-allocated 2)
//    SizesAndStrides sizes (pre-allocated 3)
//    SizesAndStrides sizes (pre-allocated 4)
//    SizesAndStrides strides (pre-allocated 0)
//    SizesAndStrides strides (pre-allocated 1)
//    SizesAndStrides strides (pre-allocated 2)
//    SizesAndStrides strides (pre-allocated 3)
//    SizesAndStrides strides (pre-allocated 4)
//    storage offset
//    numel
//    data type, device, is_contiguous, storage_access_should_throw_, bitfields
//    DispatchKeySet
//

// Various preprocessor macros we use to check that the
// TensorImpl size hasn't changed unexpectedly. We undef
// these later.
#ifndef __NVCC__
#define C10_NVCC 0
#else
#define C10_NVCC __NVCC__
#endif

#ifndef __CUDA_VER_MAJOR__
#define C10_CUDA_VERSION_MAJOR 0
#else
#define C10_CUDA_VERSION_MAJOR __CUDA_VER_MAJOR__
#endif

#ifndef CUDA_VERSION
#define C10_CUDA_VERSION 0
#else
#define C10_CUDA_VERSION CUDA_VERSION
#endif

#ifndef __clang_major__
#define C10_CLANG_MAJOR_VERSION 0
#else
#define C10_CLANG_MAJOR_VERSION __clang_major__
#endif

#ifndef __GNUC__
#define C10_GCC_VERSION 0
#else
#define C10_GCC_VERSION __GNUC__
#endif

#ifndef __GNUC_MINOR__
#define C10_GCC_VERSION_MINOR 0
#else
#define C10_GCC_VERSION_MINOR __GNUC_MINOR__
#endif

// We use a templatized class to both contain the logic of checking the sizes
// as well as to provide compile-time information that might be useful in
// figuring out why sizes may have changed.
// All the compile time information is given by the template fields that are
// always printed by the compiler when the static_assert fails.
template <
    size_t cplusplus = __cplusplus,
    size_t clang_ver_major = C10_CLANG_MAJOR_VERSION,
    size_t gcc_ver = C10_GCC_VERSION,
    size_t gcc_ver_minor = C10_GCC_VERSION_MINOR,
    size_t nvcc = C10_NVCC,
    size_t cuda_version = C10_CUDA_VERSION,
    size_t cuda_version_major = C10_CUDA_VERSION_MAJOR,
    size_t ptr_size = sizeof(void*)>
class C10_TensorImpl_Size_Check_Dummy_Class : private TensorImpl {
  // Names of (non-bitfield) fields in TensorImpl; used to provide
  // compile-time info about fields whose size changes unexpectedly.
  enum class FieldNameEnum {
    storage_,
    autograd_meta_,
    extra_meta_,
    version_counter_,
    pyobj_slot_,
    sizes_and_strides_,
    storage_offset_,
    numel_,
    data_type_,
    device_opt_,
    key_set_,
    TOTAL_SIZE
  };

  // Provides compile-time equality check that reveals what numbers
  // were used and on which quantity
  template <size_t Actual, size_t Expected, FieldNameEnum FiledName>
  constexpr static bool are_equal() {
    static_assert(
        Actual == Expected,
        "Actual and Expected sizes of a field did not match!");
    return true;
  }

  // Provides compile-time <= check that reveals what numbers
  // were used and on which quantity
  template <size_t Actual, size_t Expected, FieldNameEnum FiledName>
  constexpr static bool is_le() {
    static_assert(
        Actual <= Expected,
        "Actual and Expected sizes of a field did not match!");
    return true;
  }

 public:
  // Compile-time check that TensorImpl field sizes are as expected
  //
  // Observed total sizes and associated versions
  // If you find a flag that predicts when unique_ptr has 16 bytes
  // on 64-bit systems or when sizes_and_strides_ is 84 vs 88 bytes
  // on 32-bit systems you get a cookie!
  // Length | LLVM | GCC  |    C++ |  CUDA
  //    192 |    ? | 11.2 | 201703 | 11040
  //    208 |    ? | 11.2 | 201703 | 11040
  //    208 |    ? | 11.2 | 201402 | 11040
  //    192 |    ? | 11.2 | 201402 | 11040
  //    160 |   12 |  4.2 | 201703 |     0
  //
  // To keep things clean, we split on systems here.

#if UINTPTR_MAX == 0xFFFFFFFF
  // This is a 32-bit system
  static constexpr bool check_sizes() {
    constexpr size_t tsize = 20 * sizeof(int64_t);

    // clang-format off
    are_equal<sizeof(storage_),            4,  FieldNameEnum::storage_>();
    are_equal<sizeof(autograd_meta_),      4,  FieldNameEnum::autograd_meta_>();
    are_equal<sizeof(extra_meta_),         4,  FieldNameEnum::extra_meta_>();
    are_equal<sizeof(version_counter_),    4,  FieldNameEnum::version_counter_>();
    are_equal<sizeof(pyobj_slot_),    4,  FieldNameEnum::pyobj_slot_>();
    is_le<sizeof(sizes_and_strides_),     88, FieldNameEnum::sizes_and_strides_>();
    are_equal<sizeof(storage_offset_),     8,  FieldNameEnum::storage_offset_>();
    are_equal<sizeof(numel_),              8,  FieldNameEnum::numel_>();
    are_equal<sizeof(data_type_),          2,  FieldNameEnum::data_type_>();
    are_equal<sizeof(device_opt_),         3,  FieldNameEnum::device_opt_>();
    are_equal<sizeof(key_set_),            8,  FieldNameEnum::key_set_>();
    is_le<sizeof(TensorImpl),          tsize,  FieldNameEnum::TOTAL_SIZE>();
    // clang-format on

    return true;
  }
#else
  // This is a 64-bit system
  static constexpr bool check_sizes() {
    constexpr size_t tsize = 26 * sizeof(int64_t);

    // clang-format off
    are_equal<sizeof(storage_),            8,  FieldNameEnum::storage_>();
    // On some systems involving NVCC the size of unique_ptr is 16 bytes. We haven't
    // figured out how to detect those via macro preprocessors yet, so we use <=
    // comparisons for the relevant fields.
    is_le<sizeof(autograd_meta_),         16,  FieldNameEnum::autograd_meta_>();
    is_le<sizeof(extra_meta_),            16,  FieldNameEnum::extra_meta_>();
    are_equal<sizeof(version_counter_),    8,  FieldNameEnum::version_counter_>();
    are_equal<sizeof(pyobj_slot_),    8,  FieldNameEnum::pyobj_slot_>();
    are_equal<sizeof(sizes_and_strides_), 88,  FieldNameEnum::sizes_and_strides_>();
    are_equal<sizeof(storage_offset_),     8,  FieldNameEnum::storage_offset_>();
    are_equal<sizeof(numel_),              8,  FieldNameEnum::numel_>();
    are_equal<sizeof(data_type_),          2,  FieldNameEnum::data_type_>();
    are_equal<sizeof(device_opt_),         3,  FieldNameEnum::device_opt_>();
    are_equal<sizeof(key_set_),            8,  FieldNameEnum::key_set_>();
    is_le<sizeof(TensorImpl),          tsize,  FieldNameEnum::TOTAL_SIZE>();
    // clang-format on

    return true;
  }
#endif
};

// We use a class to encapsulate size-checking logic with
// templates to capture sizes and flags. We call this within
// a static assert to prove there is no run-time behaviour.
// Since the methods we call return either true or fail their
// own static_asserts, we should never see the error messages
// below. We have to provide it though for c++ <17.
static_assert(
    C10_TensorImpl_Size_Check_Dummy_Class<>::check_sizes(),
    "You should not see this message.");

// Clean up after ourselves
#undef C10_NVCC
#undef C10_CUDA_VERSION_MAJOR
#undef C10_CUDA_VERSION
#undef C10_CLANG_MAJOR_VERSION
#undef C10_GCC_VERSION
#undef C10_GCC_VERSION_MINOR

} // namespace c10

C10_DIAGNOSTIC_POP()

#else
#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)