if (TF_GetCode(status) != TF_OK) return;

  // unwrap op_ and set step_id only if valid step id value was set.
  // Currently only required for non-TFRT use cases, e.g., EagerOp.
  if (step_id_.has_value()) {
    tensorflow::unwrap(op_.get())->SetStepId(step_id_.value());
  }

  TFE_Execute(op_.get(), unwrapped_results.data(), &real_num_outputs, status);
  if (TF_GetCode(status) != TF_OK) {

      maybe_parallel_results(
          parallel_device.Execute(context, parallel_inputs, operation_name,
                                  attributes, expected_max_outputs, status));
  if (!maybe_parallel_results.has_value()) return result;
  std::vector<std::unique_ptr<ParallelTensor>> parallel_results(
      std::move(maybe_parallel_results.value()));
  std::vector<MaybeParallelTensorOwned> result_content;

  DLManagedTensor* dlmt = static_cast<DLManagedTensor*>(dlm);
  DLTensor* dl_tensor = &dlmt->dl_tensor;
  absl::optional<std::string> device_name =
      DeviceNameFromDlContext(dl_tensor->device, status);
  if (!device_name.has_value()) {
    status->status =
        tensorflow::errors::InvalidArgument("Unsupported Device Type");
    return nullptr;
  }
  TF_DataType dtype;
  absl::Status s = TfDataTypeFormDlDataType(dl_tensor->dtype, &dtype);

      tensorflow::EvaluateConstantTensorRunner{
          graph->graph.op_registry(),
          graph->graph.versions().producer(),
      });
  if (!status_or.ok() || !status_or->has_value()) {
    *result = nullptr;
    status->status = std::move(status_or).status();
    return false;
  }
  *result = TF_TensorFromTensor(**status_or, &status->status);
  return status->status.ok();
}