def METHOD_NAME(self):
    """This is neural_compressor function include tuning, export and benchmark option."""
    from neural_compressor import set_random_seed
    set_random_seed(9527)
    if args.tune:
        from neural_compressor import quantization
        from neural_compressor.data import DataLoader
        from neural_compressor.config import PostTrainingQuantConfig  
        dataset = CustomDataset()
        calib_dataloader=DataLoader(framework='tensorflow', dataset=dataset, \
                                    batch_size=1, collate_fn = collate_function)          
        conf = PostTrainingQuantConfig()
        q_model = quantization.fit(args.input_graph, conf=conf, \
                                   calib_dataloader=calib_dataloader, eval_func=evaluate)
        q_model.save(args.output_graph)
    if args.benchmark:
        if args.mode == 'performance':
            from neural_compressor.benchmark import fit
            from neural_compressor.config import BenchmarkConfig
            conf = BenchmarkConfig()
            fit(args.input_graph, conf, b_func=evaluate)
        elif args.mode == 'accuracy':
            acc_result = evaluate(args.input_graph)
            print("Batch size = %d" % args.batch_size)
            print("Accuracy: %.5f" % acc_result)