const int64_t* const* dims, const int* num_dims,
                         int num_shapes) {
  std::vector<PartialTensorShape> shapes;
  shapes.reserve(num_shapes);
  for (int i = 0; i < num_shapes; ++i) {
    if (num_dims[i] < 0) {
      shapes.emplace_back();
    } else {
      shapes.emplace_back(ArraySlice<int64_t>(
          reinterpret_cast<const int64_t*>(dims[i]), num_dims[i]));
    }
  }

  // A moved-from Cleanup can be safely destroyed or reassigned.
  template <typename G>
  Cleanup(Cleanup<G>&& src)  // NOLINT
      : released_(src.is_released()), f_(src.release()) {}

  // Assignment to a Cleanup object behaves like destroying it
  // and making a new one in its place, analogous to unique_ptr
  // semantics.
  Cleanup& operator=(Cleanup&& src) {  // NOLINT
    if (!released_) f_();
    released_ = src.released_;

TF_CAPI_EXPORT extern const char* TFE_TensorHandleBackingDeviceName(
    TFE_TensorHandle* h, TF_Status* status);

// Return a pointer to a new TFE_TensorHandle that shares the underlying tensor
// with `h`. On success, `status` is set to OK. On failure, `status` reflects
// the error and a nullptr is returned.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_TensorHandleCopySharingTensor(

// Delete GraphProperties.
TF_CAPI_EXPORT extern void TF_DeleteGraphProperties(
    TF_GraphProperties* graph_properties);

// Infer tensor shapes through abstract interpretation.
// If assume_valid_feeds is true, it can help infer shapes in the fanout of fed
// nodes. This may cause incorrectness in graph analyses, but is useful for
// simulation or scheduling.

  int version = TFE_CUSTOM_DEVICE_VERSION;

  // Computes the rank of the tensor handle.
  //
  // Shapes are specified via callbacks because retrieving the shape of a tensor
  // is a blocking operation for async eager; custom devices should avoid
  // retrieving shapes of tensors they wrap until the custom device tensor's
  // shape is explicitly requested where possible.

    visibility = ["//tensorflow/python:__pkg__"],
    deps = [
        ":c_api",
        ":c_api_internal",
        "//tensorflow/core:protos_all_cc",
        # TODO(b/74620627): remove when _USE_C_SHAPES is removed
    ],
    alwayslink = 1,
)

cc_library(
    name = "conversion_macros",
    hdrs = [
        "conversion_macros.h",
    ],

namespace tensorflow {

// Set the shapes and types of the output's handle.
//
// The lengths of the arrays pointed to by `shapes`, `ranks`, and `types` must
// all be equal to `num_shapes_and_types`. If `ranks[i] != -1`, (i.e., if the
// rank is known), then it must be equal to the length of `shapes[i]`; if
// `ranks[i] == 1`, then `shapes[i]` may be nullptr.
//

 public:
  CheckpointReader(const string& filename, TF_Status* status);

  bool HasTensor(const string& name) const;
  const string DebugString() const;

  // Returns a map from variable names to their shapes.  Slices of a partitioned
  // tensor are combined into a single entry.
  const TensorSliceReader::VarToShapeMap& GetVariableToShapeMap() const;

  // Returns a map from variable names to their data types.  Slices of a

  //
  // -1 indicates an unknown axis length; this is unreachable for most standard
  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.
  virtual Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.
  virtual const char* DeviceName(Status* status) const = 0;

  EXPECT_EQ(TF_FLOAT, TFE_TensorHandleDataType(h));

  TFE_TensorHandle* h_shares_tensor =
      TFE_TensorHandleCopySharingTensor(h, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  TF_Tensor* t = TFE_TensorHandleResolve(h_shares_tensor, status.get());
  ASSERT_EQ(16, TF_TensorByteSize(t));
  float data[4] = {0};