def METHOD_NAME(_):
    from neural_compressor import set_random_seed
    set_random_seed(9527)
    if FLAGS.tune:
        from neural_compressor import quantization
        from neural_compressor.config import PostTrainingQuantConfig
        conf = PostTrainingQuantConfig(backend='itex',
                calibration_sampling_size=[50, 100])
        q_model = quantization.fit(FLAGS.input_model, conf=conf, calib_dataloader=calib_dataloader,
                    eval_func=evaluate)
        q_model.save(FLAGS.output_model)
    if FLAGS.benchmark:
        from neural_compressor.benchmark import fit
        from neural_compressor.config import BenchmarkConfig
        if FLAGS.mode == 'performance':
            conf = BenchmarkConfig(backend='itex',
                    warmup=10, iteration=100, cores_per_instance=4, num_of_instance=1)
            fit(FLAGS.input_model, conf, b_func=evaluate)
        else:
            from neural_compressor.model import Model
            model = Model(FLAGS.input_model, backend='itex').model
            accuracy = evaluate(model)
            print('Batch size = %d' % FLAGS.batch_size)
            print("Accuracy: %.5f" % accuracy)