def METHOD_NAME(_):
    if FLAGS.tune:
        from neural_compressor.quantization import fit
        from neural_compressor.config import PostTrainingQuantConfig
        from neural_compressor import set_random_seed
        set_random_seed(9527)
        config = PostTrainingQuantConfig(backend='itex', 
            calibration_sampling_size=[50, 100])
        q_model = fit(
            model=FLAGS.input_model,
            conf=config,
            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', cores_per_instance=4, num_of_instance=1)
            fit(FLAGS.input_model, conf, b_func=evaluate)
        else:
            from neural_compressor.model.model import Model
            accuracy = evaluate(Model(FLAGS.input_model, backend='itex').model)
            logger.info('Batch size = %d' % FLAGS.batch_size)
            logger.info("Accuracy: %.5f" % accuracy)