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;
  }
  return new TFE_TensorDebugInfo(dev_dims);
}

                  api_def_overwrites.size(), status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);

  string op_name = "TestCApi";
  status = TF_NewStatus();
  auto* api_def_buf =
      TF_ApiDefMapGet(api_def_map, op_name.c_str(), op_name.size(), status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);

  tensorflow::ApiDef api_def;

namespace gradients {

using namespace std;

// ================== Helper functions =================

// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,
           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {
    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.

  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpAddInput(op.get(), first, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpAddInput(op.get(), second, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  const char* first_device = TFE_TensorHandleDeviceName(first, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetDevice(op.get(), first_device, status);

int64_t ParallelTensorDim(void* data, int dim_index, TF_Status* status) {
  const std::vector<int64_t>* shape;
  Status s = reinterpret_cast<ParallelTensor*>(data)->Shape(&shape);
  if (!s.ok()) {
    tsl::Set_TF_Status_from_Status(status, s);
    return -1;
  }
  return (*shape)[dim_index];
}

TF_Buffer* ParallelTensorSummarize(void* data, TF_Status* status) {

  tf_writable_file::Append(file, content + 4, 4, status_);
  ASSERT_TF_OK(status_);
  length = tf_writable_file::Tell(file, status_);
  ASSERT_EQ(length, 8);
  ASSERT_TF_OK(status_);
  tf_writable_file::Flush(file, status_);
  ASSERT_TF_OK(status_);
  ASSERT_TRUE(
      CompareWithServer(filepath, 0, 8, &gcs_file->gcs_client, status_));

  tf_writable_file::Close(file, status_);
  ASSERT_TF_OK(status_);

  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  TFE_OpSetDevice(op.get(), device_name, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  TFE_OpAddInput(op.get(), parallel_value.get(), status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  TFE_TensorHandle* raw_result_handle;
  int num_retvals = 1;

void TF_GraphCopyFunction(TF_Graph* g, const TF_Function* func,
                          const TF_Function* grad, TF_Status* status) {
  if (func == nullptr) {
    status->status = InvalidArgument(
        "'func' argument to TF_GraphCopyFunction cannot be null");
    return;
  }

  tensorflow::mutex_lock l(g->mu);
  status->status = g->graph.AddFunctionDef(func->record->fdef(),

};

int64_t LoggedTensorDim(void* data, int dim_index, TF_Status* status) {
  return TFE_TensorHandleDim(reinterpret_cast<LoggedTensor*>(data)->tensor,
                             dim_index, status);
}

int LoggedTensorNumDims(void* data, TF_Status* status) {
  return TFE_TensorHandleNumDims(reinterpret_cast<LoggedTensor*>(data)->tensor,
                                 status);
}

void LoggedTensorDeallocator(void* data) {

 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());
    TF_SetTracingImplementation(std::get<0>(GetParam()), status.get());
    Status s = StatusFromTF_Status(status.get());
    CHECK_EQ(errors::OK, s.code()) << s.message();
  }
};

Status RegisterGradients(GradientRegistry* registry) {
  TF_RETURN_IF_ERROR(RegisterNotDifferentiable(registry, "CheckNumerics"));