def METHOD_NAME():
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--task_name",
        default=None,
        type=str,
        required=True,
        help="The name of the task to perform predict, selected in the list: " + ", ".join(METRIC_CLASSES.keys()),
    )
    parser.add_argument(
        "--model_type",
        default=None,
        type=str,
        required=True,
        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
    )
    parser.add_argument(
        "--model_path",
        default=None,
        type=str,
        required=True,
        help="The path prefix of inference model to be used.",
    )
    parser.add_argument(
        "--device",
        default="gpu",
        choices=["gpu", "cpu", "xpu"],
        help="Device selected for inference.",
    )
    parser.add_argument(
        "--batch_size",
        default=32,
        type=int,
        help="Batch size for predict.",
    )
    parser.add_argument(
        "--max_seq_length",
        default=128,
        type=int,
        help="The maximum total input sequence length after tokenization. Sequences longer "
        "than this will be truncated, sequences shorter will be padded.",
    )
    args = parser.METHOD_NAME()
    return args