How to use the cogdl.options.get_training_parser function in cogdl

[variant]
            + list(
                itertools.starmap(
                    lambda x, y: f"{x:.4f}±{y:.4f}",
                    zip(
                        np.mean(results, axis=0).tolist(),
                        np.std(results, axis=0).tolist(),
                    ),
                )
            )
        )
    return tab_data


if __name__ == "__main__":
    parser = options.get_training_parser()
    args, _ = parser.parse_known_args()
    args = options.parse_args_and_arch(parser, args)
    print(args)
    assert len(args.device_id) == 1
    variants = list(
        gen_variants(dataset=args.dataset, model=args.model, seed=args.seed)
    )

    # Collect results
    results_dict = defaultdict(list)
    results = [main(args) for args in variant_args_generator(args, variants)]
    for variant, result in zip(variants, results):
        results_dict[variant[:-1]].append(result)

    col_names = ["Variant"] + list(results_dict[variant[:-1]][-1].keys())
    tab_data = tabulate_results(results_dict)

def gen_variants(**items):
    Variant = namedtuple("Variant", items.keys())
    return itertools.starmap(Variant, itertools.product(*items.values()))


def getpid(_):
    # HACK to get different pids
    time.sleep(1)
    return mp.current_process().pid


if __name__ == "__main__":
    # Magic for making multiprocessing work for PyTorch
    mp.set_start_method("spawn")

    parser = options.get_training_parser()
    args, _ = parser.parse_known_args()
    args = options.parse_args_and_arch(parser, args)

    # Make sure datasets are downloaded first
    datasets = args.dataset
    for dataset in datasets:
        args.dataset = dataset
        _ = build_dataset(args)
    args.dataset = datasets

    print(args)
    variants = list(
        gen_variants(dataset=args.dataset, model=args.model, seed=args.seed)
    )

    device_ids = args.device_id

[variant]
            + list(
                itertools.starmap(
                    lambda x, y: f"{x:.4f}±{y:.4f}",
                    zip(
                        np.mean(results, axis=0).tolist(),
                        np.std(results, axis=0).tolist(),
                    ),
                )
            )
        )
    return tab_data


if __name__ == "__main__":
    parser = options.get_training_parser()
    args, _ = parser.parse_known_args()
    args = options.parse_args_and_arch(parser, args)
    print(args)
    assert len(args.device_id) == 1
    variants = list(
        gen_variants(dataset=args.dataset, model=args.model, seed=args.seed)
    )

    # Collect results
    results_dict = defaultdict(list)
    results = [main(args) for args in variant_args_generator(args, variants)]
    for variant, result in zip(variants, results):
        results_dict[variant[:-1]].append(result)

    col_names = ["Variant"] + list(results_dict[variant[:-1]][-1].keys())
    tab_data = tabulate_results(results_dict)