def METHOD_NAME():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--task_cache_dir", type=str, help="Please provide the full path to the extracted tasks."
    )
    parser.add_argument(
        "--candidate_cache_dir",
        type=str,
        help="Please provide the full path to save the sampled candidates.",
    )
    parser.add_argument(
        "--target",
        type=str,
        default="nvidia/geforce-rtx-3070",
        help="Please specify the target hardware for tuning.\
                    Note: for generating dataset, the hardware does not need to be present.",
    )
    parser.add_argument(
        "--init_population_size",
        type=int,
        default=256,
        help="The initial population size used in evolutionary search.",
    )
    parser.add_argument(
        "--num_samples_per_task",
        type=int,
        default=400,
        help="The number of samples to gather per tuning task.",
    )
    parser.add_argument(
        "--num_trials_per_iter",
        type=int,
        default=64,
        help="The number of trials per iteration in evolutionary search.",
    )
    parser.add_argument(
        "--max_trials_per_task",
        type=int,
        default=400,
        help="The maximum number of trials per task in evolutionary search.",
    )
    parser.add_argument(
        "--max_retry_per_task",
        type=int,
        default=10,
        help="The maximum number of retry attempts allowed.",
    )
    parser.add_argument(
        "--file_group",
        type=int,
        default=0,
        help="To enable running multiple scripts in parallel, files [idx * 10 : (idx + 1) * 10]\
        in the sorted file list from the given directory will be run.",
    )
    return parser.parse_args()