std::vector<TFE_TensorHandle*> result;
    result.reserve(tensors_.size());
    for (const TensorHandlePtr& tensor : tensors_) {
      result.emplace_back(tensor.get());
    }
    return result;
  }

 private:
  ParallelTensor(const ParallelDevice& device,
                 std::vector<TensorHandlePtr> tensors,
                 absl::Span<const int64_t> shape, const TF_DataType dtype)

                       const string& op_name = "");
  // Returns whether any tensor in a list of tensors is being watched and has
  // a trainable dtype.
  bool ShouldRecord(
      absl::Span<const AbstractTensorHandle* const> tensors) const;
  // Unwatches this tensor on the tape. Mainly used for cleanup when deleting
  // eager tensors.
  void DeleteTrace(const AbstractTensorHandle*);

      });
}
bool Tape::ShouldRecord(
    absl::Span<const AbstractTensorHandle* const> tensors) const {
  std::vector<int64_t> tensor_ids(tensors.size());
  std::vector<tensorflow::DataType> tensor_dtypes(tensors.size());
  for (int i = 0; i < tensors.size(); i++) {
    tensor_ids[i] = ToId(tensors[i]);
    tensor_dtypes[i] = tensors[i]->DataType();
  }
  return GradientTape::ShouldRecord(tensor_ids, tensor_dtypes);
}

    status->status = tensorflow::errors::InvalidArgument(
        "DLPack doesn't support ", handle->TypeString(), " tensor");
    return nullptr;
  }
  const tensorflow::Tensor* tensor;
  status->status = handle->Tensor(&tensor);
  if (!status->status.ok()) {
    return nullptr;
  }
  return tensor;
}

// Deleter for DLManagedTensor
void DLManagedTensorDeleter(DLManagedTensor* arg) {
  TfDlManagedTensorCtx* owner =

#ifndef TENSORFLOW_C_EAGER_DLPACK_H_
#define TENSORFLOW_C_EAGER_DLPACK_H_

#include "tensorflow/c/eager/c_api.h"

namespace tensorflow {

// PyCapsule name for DLPack Tensor
const char* const kDlTensorCapsuleName = "dltensor";

// Converts eager tensor handle to DLPack (DLManagedTensor*), and return the
// void* for further PyCapsule construction.
TF_CAPI_EXPORT extern void* TFE_HandleToDLPack(TFE_TensorHandle* h,

                                       TFE_TensorHandle* tensor,
                                       TF_Status* status, void* device_info) {
  TF_SetStatus(
      status, TF_UNIMPLEMENTED,
      absl::StrCat("Trying to copy a tensor ",
                   tensorflow::unwrap(tensor)->DebugString(),
                   " on to a parallel device. Pack non-parallel "
                   "tensors for each device into a parallel tensor explicitly.")

                                                   TF_Status* status);

// A handle to a tensor on a device.
//
// Like a TF_Tensor, a TFE_TensorHandle refers to a tensor with a value, shape,
// type etc. Unlike a TF_Tensor, a TFE_TensorHandle may refer to such tensors
// placed in the memory of different devices or remote address spaces.
typedef struct TFE_TensorHandle TFE_TensorHandle;

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TFE_DeleteContextOptions(opts);

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Build an abstract input tensor.
  TFE_Context* eager_ctx = TF_ExecutionContextGetTFEContext(ctx, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TFE_TensorHandle* t = TestScalarTensorHandle(eager_ctx, 2.0f);

    TFE_TensorHandle* h, TF_Status* status) {
  tensorflow::TensorHandle* handle =
      TensorHandleFromInterface(tensorflow::unwrap(h));
  const tensorflow::Tensor* tensor;
  status->status = handle->Tensor(&tensor);
  if (!status->status.ok()) {
    return nullptr;
  }

  std::vector<int64_t> dev_dims = TensorShapeAsVector(*handle, &status->status);
  if (!status->status.ok()) {
    return nullptr;

  // ops are implemented.
  //===--------------------------------------------------------------------===//
  // Create an abstract tensor handle from tensorflow::Tensor.
  virtual ImmediateExecutionTensorHandle* CreateLocalHandleFromTFTensor(
      tensorflow::Tensor& t, const char* d_name) = 0;

  // Convert a TFRT TensorHandle to tensorflow::TensorHandle.