def METHOD_NAME():
    global config
    config = load_config(FLAGS.config_path)
    ptq_config = config['PTQ']
    # val dataset is sufficient for PTQ
    data_loader = create('EvalReader')(
        config['EvalDataset'], config['worker_num'], return_list=True)
    ptq_data_loader = reader_wrapper(data_loader, config['input_list'])
    # fast_val_anno_path, such as annotation path of several pictures can accelerate analysis
    dataset = config[
        'FastEvalDataset'] if 'FastEvalDataset' in config else config[
            'EvalDataset']
    global val_loader
    _eval_batch_sampler = paddle.io.BatchSampler(
        dataset, batch_size=config['EvalReader']['batch_size'])
    val_loader = create('EvalReader')(
        dataset,
        config['worker_num'],
        batch_sampler=_eval_batch_sampler,
        return_list=True)
    global metric
    if config['metric'] == 'COCO':
        clsid2catid = {v: k for k, v in dataset.catid2clsid.items()}
        anno_file = dataset.get_anno()
        metric = COCOMetric(
            anno_file=anno_file, clsid2catid=clsid2catid, IouType='bbox')
    elif config['metric'] == 'VOC':
        metric = VOCMetric(
            label_list=dataset.get_label_list(),
            class_num=config['num_classes'],
            map_type=config['map_type'])
    elif config['metric'] == 'KeyPointTopDownCOCOEval':
        anno_file = dataset.get_anno()
        metric = KeyPointTopDownCOCOEval(anno_file,
                                         len(dataset), 17, 'output_eval')
    else:
        raise ValueError("metric currently only supports COCO and VOC.")
    analyzer = Analysis(
        float_model_dir=config["model_dir"],
        model_filename=config["model_filename"],
        params_filename=config["params_filename"],
        eval_function=eval_function,
        data_loader=ptq_data_loader,
        save_dir=config['save_dir'],
        quant_config=ptq_config,
        resume=True, )
    analyzer.statistical_analyse()
    analyzer.metric_error_analyse()
    if config['get_target_quant_model']:
        if 'FastEvalDataset' in config:
            # change fast_val_loader to full val_loader
            val_loader = data_loader
        # get the quantized model that satisfies target metric you set
        analyzer.get_target_quant_model(target_metric=config['target_metric'])