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"

                              /*expected_max_outputs=*/1, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  const std::vector<std::unique_ptr<ParallelTensor>>& handles = *outputs;
  std::vector<ParallelTensor*> handle_inputs;
  handle_inputs.reserve(handles.size());
  for (auto& handle : handles) {
    handle_inputs.push_back(handle.get());
  }

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

#include <memory>

#include "tensorflow/core/framework/full_type.pb.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"

  // Delete the variable handle itself.
  TFE_DeleteTensorHandle(handle_);
}

TensorHandlePtr Variable::Read(TFE_Context* context, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context, "ReadVariableOp", status), &TFE_DeleteOp);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpAddInput(op.get(), handle_, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;

    TFE_OpAddInput(op, var_handle, status);
    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_Execute(op, &handle_1, &num_retvals, status);
    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_DeleteOp(op);

    ASSERT_EQ(1, num_retvals);
    EXPECT_EQ(TF_FLOAT, TFE_TensorHandleDataType(handle_1));
    EXPECT_EQ(0, TFE_TensorHandleNumDims(handle_1, status));

  TFE_CustomDeviceTensorHandleMethods handle_methods;
  handle_methods.num_dims = &LoggedTensorNumDims;
  handle_methods.dim = &LoggedTensorDim;
  handle_methods.deallocator = &LoggedTensorDeallocator;
  return TFE_NewCustomDeviceTensorHandle(context, logging_device_name.c_str(),
                                         dtype, t.release(), handle_methods,
                                         status);
}

}

TapeTensor::TapeTensor(AbstractTensorHandle* handle) : handle_(handle) {
  handle_->Ref();
}
TapeTensor::TapeTensor(const TapeTensor& other) {
  handle_ = other.handle_;
  handle_->Ref();
}
TapeTensor::~TapeTensor() { handle_->Unref(); }

int64_t TapeTensor::GetID() const { return ToId(handle_); }

tensorflow::DataType TapeTensor::GetDType() const {
  return handle_->DataType();
}

  ParallelTensor* t_released = t.release();
  TFE_CustomDeviceTensorHandleMethods handle_methods;
  handle_methods.num_dims = &ParallelTensorNumDims;
  handle_methods.dim = &ParallelTensorDim;
  handle_methods.deallocator = &ParallelTensorDeallocator;
  handle_methods.summarize = &ParallelTensorSummarize;
  return TensorHandlePtr(TFE_NewCustomDeviceTensorHandle(

    // Read the value of tensor handle `handle_1`.
    float value = 0.0f;
    TF_Tensor* t = TFE_TensorHandleResolve(handle_1, status);
    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    ASSERT_EQ(sizeof(float), TF_TensorByteSize(t));
    memcpy(&value, TF_TensorData(t), sizeof(float));
    TF_DeleteTensor(t);
    EXPECT_EQ(1.2f, value);
    TFE_DeleteTensorHandle(handle_1);
    TF_DeleteStatus(status);

#     # and LessThan at PRODUCT.PRODUCT_NAME and PRODUCT.PRODUCT_HANDLE_CODE,
#     # and InScope for LONGVARCHAR(e.g. text type) is excluded.
#     ; String = map:{
#         ; GreaterThan = map:{ MEMBER = list:{ MEMBER_ACCOUNT } }
#         ; LessThan = map:{ PRODUCT = list:{ PRODUCT_NAME ; PRODUCT_HANDLE_CODE } }
#         ; !InScope = map:{ $$ALL$$ = list:{ type:LONGVARCHAR } }
#     }