"//tensorflow/core:framework_internal",
        "//tensorflow/core/lib/io:path",
        "//tensorflow/core/platform:env",
        "//tensorflow/core/platform:errors",
        "//tensorflow/core/platform:stacktrace_handler",
        "//tensorflow/core/platform:test",
    ],

      MemoryReleaser memory_releaser, void* memory_releaser_arg) = 0;

  // Create a handle to wrap and manage a Tensor
  virtual ImmediateExecutionTensorHandle* CreateLocalHandle(
      AbstractTensorInterface* t) = 0;
  // Copy the handle to another device.
  virtual ImmediateExecutionTensorHandle* CopyTensorHandleToDevice(
      ImmediateExecutionTensorHandle* handle, const char* device_name,
      Status* status) = 0;

        ->DevicePointer();
  }
  // TODO(b/175427838): It would be nice to be able to use tensorflow::isa here.
  if (!tensorflow::TensorHandle::classof(unwrapped_handle)) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;
  }
  tensorflow::TensorHandle* handle =
      tensorflow::TensorHandleFromInterface(unwrapped_handle);

fi

# Generate an overview page describing the build
if [[ "$TFCI_INDEX_HTML_ENABLE" == 1 ]]; then
  ./ci/official/utilities/generate_index_html.sh "$TFCI_OUTPUT_DIR/index.html"
fi

# Single handler for all cleanup actions, triggered on an EXIT trap.
# TODO(angerson) Making this use different scripts may be overkill.
cleanup() {
  if [[ "$TFCI_DOCKER_ENABLE" == 1 ]]; then
    ./ci/official/utilities/cleanup_docker.sh

        "//tensorflow/core/kernels:dataset_ops",
        "//tensorflow/core/platform:logging",
        "//tensorflow/core/platform:path",
        "//tensorflow/core/platform:stacktrace_handler",
        "//tensorflow/core/platform:statusor",
        "//tensorflow/core/platform:stringpiece",
        "//tensorflow/core/platform:types",
        "//tensorflow/core/profiler/internal:print_model_analysis",

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"

  virtual AbstractTensorInterface* Resolve(Status* status) = 0;

  std::string DebugString() const override;

  // Returns a Boolean hint indicating whether callers should prefer
  // `SummarizeValue` to resolving this handle and formatting the tensor.
  //
  // For example some tensor handles may represent distributed values, in which

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()) {

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

  TFE_TensorHandle* packed_handle = nullptr;
  if (has_packed_input) {
    int num_replicas = 1;
    std::vector<TFE_TensorHandle*> packed_handles = {h1_task2};
    packed_handle = TFE_CreatePackedTensorHandle(ctx, packed_handles.data(),
                                                 &num_replicas, status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  }

  virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.
  // Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

  virtual Status InputLength(const char* input_name, int* length) = 0;