def METHOD_NAME():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name",
        default="gpt-cpm-large-cn",
        choices=list(MODEL_CLASSES.keys()),
        help="The model name to specify which gpt to use. It can be " + ", ".join(MODEL_CLASSES.keys()),
    )
    parser.add_argument("--batch_size", default=4, type=int, help="Batch size.")
    parser.add_argument("--max_length", default=50, type=int, help="Maximum output length.")
    parser.add_argument(
        "--topk", default=1, type=int, help="The number of highest probability tokens to keep for top-k-sampling."
    )
    parser.add_argument("--topp", default=1.0, type=float, help="The cumulative probability for top-p-filtering.")
    parser.add_argument("--temperature", default=1.0, type=float, help="The temperature to set.")
    parser.add_argument("--tensor_para_size", default=2, type=int, help="The size for tensor parallel.")
    parser.add_argument("--layer_para_size", default=1, type=int, help="The size for layer parallel.")
    parser.add_argument(
        "--layer_para_batch_size",
        default=None,
        type=int,
        help="The local batch size for pipeline parallel." "It is suggested to use `batch_size // layer_para_size`.",
    )
    parser.add_argument("--use_fp16", action="store_true", help="Whether to use fp16 to predict.")
    parser.add_argument("--profile", action="store_true", help="Whether to profile.")
    args = parser.METHOD_NAME()
    return args