See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

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

namespace tensorflow {

std::string AbstractTensorHandle::DebugString() const {
  PartialTensorShape shape;
  Status s = Shape(&shape);
  std::string shape_string;
  if (!s.ok()) {

  // Returns the status of the tensor handle. If it is a tfrt::TensorHandle,
  // the tensor handle can be an error and return non-OK status.
  virtual tensorflow::Status TensorHandleStatus() const;

  // Returns tensor shape. If tensor has unknown rank, shape remains untouched.
  virtual tensorflow::Status Shape(
      tensorflow::PartialTensorShape* shape) const = 0;

  // Returns tensor (full) type.

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_TENSOR_HANDLE_H_
#define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_TENSOR_HANDLE_H_

#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/tensor_interface.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

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

namespace tensorflow {

std::string ImmediateExecutionTensorHandle::DebugString() const {
  PartialTensorShape shape;
  std::string shape_string;
  if (Shape(&shape).ok()) {

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_TFE_TENSOR_DEBUG_INFO_INTERNAL_H_
#define TENSORFLOW_C_EAGER_TFE_TENSOR_DEBUG_INFO_INTERNAL_H_

#include <vector>

#include "tensorflow/core/platform/types.h"

struct TFE_TensorDebugInfo {
  explicit TFE_TensorDebugInfo(const std::vector<int64_t>& dims)

      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

The MIT License (MIT)

Copyright (c) 2014 Sam Ghods

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

  }

  /**
   * Explicitly specifies the return type of this {@code Invokable}. For example:
   *
   * <pre>{@code
   * Method factoryMethod = Person.class.getMethod("create");
   * Invokable<?, Person> factory = Invokable.of(getNameMethod).returning(Person.class);
   * }</pre>
   */
  public final <R1 extends R> Invokable<T, R1> returning(Class<R1> returnType) {
    return returning(TypeToken.of(returnType));
  }

  // Convert from AbstractTensor to TF_Tensor.
  TF_Tensor* theta_tensor;
  TF_RETURN_IF_ERROR(GetValue(theta, &theta_tensor));

  // Get number of elements and fill data.
  int num_elems = TF_TensorElementCount(theta_tensor);
  vector<float> theta_data(num_elems);
  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.

  ParallelTensor* parallel_tensor = reinterpret_cast<ParallelTensor*>(
      TFE_TensorHandleDevicePointer(tensor, status));
  if (TF_GetCode(status) != TF_OK) return nullptr;
  if (parallel_tensor->num_tensors() == 1) {
    // Copy-off for single-device tensors is allowed to make debugging dynamic
    // control flow easier.
    return TFE_TensorHandleCopySharingTensor(parallel_tensor->tensor(0),