tests := []struct {
		name                    string
		args                    *config.DefaultPreemptionArgs
		nodeNames               []string
		testPods                []*v1.Pod
		initPods                []*v1.Pod
		registerPlugins         []tf.RegisterPluginFunc
		pdbs                    []*policy.PodDisruptionBudget
		fakeFilterRC            framework.Code // return code for fake filter plugin

      }

      self._save_tf1_model(
          sess,
          output_path,
          signature_def_key,
          tags,
          inputs=inputs,
          outputs=outputs,
          init_op=lookup_ops.tables_initializer(),
          assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS),
      )

    return inputs, outputs

  def _create_and_save_file_init_hash_table_model_tf1(

    OpsTestBase::SetDevice(DEVICE_GPU, std::move(device));
#endif
    TF_ASSERT_OK(NodeDefBuilder(op_name, op_name).Finalize(node_def()));
    TF_ASSERT_OK(InitOp());
  }

#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
  const char* device_name_ = tensorflow::DEVICE_GPU;
#else
  const char* device_name_ = tensorflow::DEVICE_CPU;
#endif
};

					return fmt.Errorf("failed to set feature gates from initial flags-based config: %w", err)
				}
			}

			// Config and flags parsed, now we can initialize logging.
			logs.InitLogs()
			if err := logsapi.ValidateAndApplyAsField(&kubeletConfig.Logging, utilfeature.DefaultFeatureGate, field.NewPath("logging")); err != nil {
				return fmt.Errorf("initialize logging: %v", err)
			}