TFE_TensorHandle* TestVariable(TFE_Context* ctx, float value,
                               const tensorflow::string& device_name) {
  TF_Status* status = TF_NewStatus();
  // Create the variable handle.
  TFE_Op* op = TFE_NewOp(ctx, "VarHandleOp", status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpSetAttrShape(op, "shape", {}, 0, status);

  TFE_ContextAddFunctionDef(ctx, function_def.data(), function_def.size(),
                            status);
  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  TFE_Op* func = TFE_NewOp(ctx, "AddVariablesFunction", status);
  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  TFE_OpAddInput(func, packed_handle, status);
  ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  if (remote) {

    return 0;
  }
  const tensorflow::Tensor* tensor;
  status->status = handle->Tensor(&tensor);
  if (!status->status.ok()) {
    return 0;
  }
  return tensor->TotalBytes();
}

TFE_Op* TFE_NewOp(TFE_Context* ctx, const char* op_or_function_name,
                  TF_Status* status) {
  tensorflow::ImmediateExecutionOperation* new_op =
      tensorflow::unwrap(ctx)->CreateOperation();

                           std::vector<TensorHandlePtr>* outputs,
                           TF_Status* status) const {
  if (op_ == nullptr) {
    TFE_ContextSetExecutorForThread(context, executor_.get());
    op_.reset(TFE_NewOp(context, operation_name, status));
    if (TF_GetCode(status) != TF_OK) return;
    TFE_OpSetDevice(op_.get(), device_.c_str(), status);
    if (TF_GetCode(status) != TF_OK) return;
  } else {