def METHOD_NAME():
    args = parse_args()
    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)
    # Build the recognizer from a config file and checkpoint file/url
    model = init_recognizer(cfg, args.checkpoint, device=args.device)
    pred_result = inference_recognizer(model, args.video)
    pred_scores = pred_result.pred_scores.item.tolist()
    score_tuples = tuple(zip(range(len(pred_scores)), pred_scores))
    score_sorted = sorted(score_tuples, key=itemgetter(1), reverse=True)
    top5_label = score_sorted[:5]
    labels = open(args.label).readlines()
    labels = [x.strip() for x in labels]
    results = [(labels[k[0]], k[1]) for k in top5_label]
    print('The top-5 labels with corresponding scores are:')
    for result in results:
        print(f'{result[0]}: ', result[1])
    if args.out_filename is not None:
        if args.target_resolution is not None:
            if args.target_resolution[0] == -1:
                assert isinstance(args.target_resolution[1], int)
                assert args.target_resolution[1] > 0
            if args.target_resolution[1] == -1:
                assert isinstance(args.target_resolution[0], int)
                assert args.target_resolution[0] > 0
            args.target_resolution = tuple(args.target_resolution)
        get_output(
            args.video,
            args.out_filename,
            pred_result,
            labels,
            fps=args.fps,
            font_scale=args.font_scale,
            font_color=args.font_color,
            target_resolution=args.target_resolution)