if (!status->status.ok()) {
    delete lib_handle;
    return nullptr;
  }
  return lib_handle;
}

TF_Buffer TF_GetOpList(TF_Library* lib_handle) { return lib_handle->op_list; }

void TF_DeleteLibraryHandle(TF_Library* lib_handle) {
  if (lib_handle == nullptr) return;
  tensorflow::port::Free(const_cast<void*>(lib_handle->op_list.data));
  delete lib_handle;
}

TF_Buffer* TF_GetAllOpList() {

  dltensor_in->strides = strides.data();
  TFE_TensorHandle* handle = TFE_HandleFromDLPack(&dlm_in, status, ctx);
  ASSERT_NE(handle, nullptr)
      << TF_Message(status) << " (shape=[" << absl::StrJoin(shape, ",")
      << "], strides=[" << absl::StrJoin(strides, ",") << "])";

  auto* dlm_out =
      static_cast<DLManagedTensor*>(TFE_HandleToDLPack(handle, status));
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

#include "tensorflow/c/experimental/gradients/nn_grad.h"

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/immediate_execution_context.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"
#include "tensorflow/c/experimental/ops/array_ops.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/experimental/ops/nn_ops.h"

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()) {

  TFE_OpAddInput(op.get(), input, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  TFE_TensorHandle* result_handle;
  int num_retvals = 1;
  TFE_Execute(op.get(), &result_handle, &num_retvals, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  return TensorHandlePtr(result_handle);
}

void TestCollective(bool async) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(

  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(

      TF_EXCLUSIVE_LOCKS_REQUIRED(execution_mutex_);

  enum class ExecutionState {
    kReadyToExecute,
    kHasResult,
    kIdle,
    kShuttingDown,
  };

  tensorflow::mutex execution_mutex_;
  ExecutionState execution_state_ TF_GUARDED_BY(execution_mutex_) =
      ExecutionState::kIdle;
  // Tells the worker thread that there is new work.
  tensorflow::condition_variable start_execute_;

  TFE_OpSetDevice(op.get(), device, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_TensorHandle* var_handle = nullptr;
  int num_retvals = 1;
  TFE_Execute(op.get(), &var_handle, &num_retvals, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  return new Variable(var_handle, type);
}

void Variable::Destroy(TFE_Context* context, TF_Status* status) {
  // Free the backing buffer for the variable.

  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);
}

  EXPECT_EQ(TFE_TensorHandleDataType(packed_handle), TF_RESOURCE);
  EXPECT_EQ(TFE_TensorHandleNumDims(packed_handle, status), 0);
  EXPECT_EQ(TFE_TensorHandleNumElements(packed_handle, status), 1);

  const string composite_device_name =
      "/job:localhost/replica:0/task:0/device:COMPOSITE:0";
  EXPECT_EQ(TFE_TensorHandleDeviceName(packed_handle, status),
            composite_device_name);