def METHOD_NAME():
    global global_config
    all_config = load_slim_config(FLAGS.config_path)
    assert "Global" in all_config, f"Key 'Global' not found in config file. \n{all_config}"
    global_config = all_config["Global"]
    ptq_config = all_config['PTQ']
    global_config['input_list'] = get_feed_vars(
        global_config['model_dir'], global_config['model_filename'],
        global_config['params_filename'])
    reader_cfg = load_config(global_config['reader_config'])
    train_loader = create('EvalReader')(reader_cfg['TrainDataset'],
                                        reader_cfg['worker_num'],
                                        return_list=True)
    train_loader = reader_wrapper(train_loader, global_config['input_list'])
    dataset = reader_cfg['EvalDataset']
    global val_loader
    _eval_batch_sampler = paddle.io.BatchSampler(
        dataset, batch_size=reader_cfg['EvalReader']['batch_size'])
    val_loader = create('EvalReader')(dataset,
                                      reader_cfg['worker_num'],
                                      batch_sampler=_eval_batch_sampler,
                                      return_list=True)
    global num_classes
    num_classes = reader_cfg['num_classes']
    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')
    global_config['metric'] = metric
    analyzer = AnalysisQuant(
        model_dir=global_config["model_dir"],
        model_filename=global_config["model_filename"],
        params_filename=global_config["params_filename"],
        eval_function=eval_function,
        data_loader=train_loader,
        resume=True,
        save_dir='output',
        ptq_config=ptq_config)
    # plot the boxplot of activations of quantizable weights
    analyzer.plot_activation_distribution()
    # get the rank of sensitivity of each quantized layer
    # plot the histogram plot of best and worst activations and weights if plot_hist is True
    analyzer.compute_quant_sensitivity(plot_hist=True)
    # get the quantized model that satisfies target metric you set
    analyzer.get_target_quant_model(target_metric=0.25)