def METHOD_NAME(_):
    tf.compat.v1.disable_eager_execution()
    tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
    from neural_compressor.metric import SquadF1
    metric = SquadF1()
    from neural_compressor.utils.create_obj_from_config import create_dataloader
    data_path = os.path.join(FLAGS.dataset_location, 'eval.tf_record')
    label_path = os.path.join(FLAGS.dataset_location, 'dev-v1.1.json')
    vocab_path = os.path.join(FLAGS.dataset_location, 'vocab.txt')
    dataloader_args = {
        'batch_size': FLAGS.batch_size,
        'dataset': {"mzbert": {'root': data_path,
                               'label_file': label_path}},
        'transform': None,
        'filter': None
    }
    dataloader = create_dataloader('tensorflow', dataloader_args)
    from neural_compressor.data import TFSquadV1ModelZooPostTransform
    postprocess = TFSquadV1ModelZooPostTransform(label_file=label_path, vocab_file=vocab_path)
    def eval(model):
        return evaluate(model, dataloader, metric, postprocess)
    if FLAGS.benchmark:
        if FLAGS.mode == 'performance':
            from neural_compressor.benchmark import fit
            from neural_compressor.config import BenchmarkConfig
            conf = BenchmarkConfig(warmup=10, iteration=100, cores_per_instance=4, num_of_instance=1)
            fit(FLAGS.input_model, conf, b_func=eval)
        elif FLAGS.mode == 'accuracy':
            acc_result = eval(FLAGS.input_model)
            print("Batch size = %d" % dataloader.batch_size)
            print("Accuracy: %.5f" % acc_result)
    elif FLAGS.tune:
        from neural_compressor import quantization
        from neural_compressor.config import PostTrainingQuantConfig
        conf = PostTrainingQuantConfig(inputs=['input_ids', 'input_mask', 'segment_ids'],
                                       outputs=['start_logits', 'end_logits'],
                                       calibration_sampling_size=[500])
        q_model = quantization.fit(FLAGS.input_model, conf=conf,
                                   calib_dataloader=dataloader, eval_func=eval)
        q_model.save(FLAGS.output_model)