def METHOD_NAME(self):
    from neural_compressor import set_random_seed
    set_random_seed(9527)
    if args.tune:
        from neural_compressor import quantization
        from neural_compressor.config import PostTrainingQuantConfig
        from neural_compressor.utils.create_obj_from_config import create_dataloader
        calib_dataloader_args = {
            'batch_size': 10,
            'dataset': {"ImageRecord": {'root':args.dataset_location}},
            'transform': {'BilinearImagenet':
                 {'height': 224, 'width': 224}},
            'filter': None
        }
        calib_dataloader = create_dataloader('tensorflow', calib_dataloader_args)
        conf = PostTrainingQuantConfig(calibration_sampling_size=[20, 50])
        q_model = quantization.fit(model=args.input_graph, conf=conf,
                                   calib_dataloader=calib_dataloader, eval_func=evaluate)
        q_model.save(args.output_graph)
    if args.benchmark:
        from neural_compressor.benchmark import fit
        from neural_compressor.config import BenchmarkConfig
        if args.mode == 'performance':
            conf = BenchmarkConfig(cores_per_instance=4, num_of_instance=1)
            from neural_compressor.utils.create_obj_from_config import create_dataloader
            dataloader_args = {
                'batch_size': args.batch_size,
                'dataset': {"ImageRecord": {'root': args.dataset_location}},
                'transform': {'BilinearImagenet': {'height': 224, 'width': 224}},
                'filter': None
            }
            eval_dataloader = create_dataloader('tensorflow', dataloader_args)
            fit(model=args.input_graph, conf=conf, b_dataloader=eval_dataloader)
        else:
            from neural_compressor.model import Model
            model = Model(args.input_graph).model
            accuracy = evaluate(model)
            print('Batch size = %d' % args.batch_size)
            print("Accuracy: %.5f" % accuracy)