def METHOD_NAME():
    parser = argparse.ArgumentParser(__doc__)
    parser.add_argument("--task_name", default=None, type=str, required=True, help="The name of the task to train.")
    parser.add_argument("--model_name_or_path", default='bert-base-uncased', type=str, help="Path to pre-trained bert model or shortcut name.")
    parser.add_argument("--output_dir", default=None, type=str, help="The output directory where the checkpoints will be saved.")
    parser.add_argument("--data_dir", default=None, type=str, help="The directory where the dataset will be load.")
    parser.add_argument("--init_from_ckpt", default=None, type=str, help="The path of checkpoint to be loaded.")
    parser.add_argument("--max_seq_len", default=None, type=int, help="The maximum total input sequence length after tokenization for trainng. Sequences longer than this will be truncated, sequences shorter will be padded.")
    parser.add_argument("--test_max_seq_len", default=None, type=int, help="The maximum total input sequence length after tokenization for testing. Sequences longer than this will be truncated, sequences shorter will be padded.")
    parser.add_argument("--batch_size", default=None, type=int, help="Batch size per GPU/CPU for training.")
    parser.add_argument("--test_batch_size", default=None, type=int, help="Batch size per GPU/CPU for testing.")
    parser.add_argument("--learning_rate", default=None, type=float, help="The initial learning rate for Adam.")
    parser.add_argument("--weight_decay", default=0.01, type=float, help="Weight decay if we apply some.")
    parser.add_argument("--epochs", default=None, type=int, help="Total number of training epochs to perform.")
    parser.add_argument("--logging_steps", default=None, type=int, help="Log every X updates steps.")
    parser.add_argument("--save_steps", default=None, type=int, help="Save checkpoint every X updates steps.")
    parser.add_argument("--seed", default=42, type=int, help="Random seed for initialization.")
    parser.add_argument("--warmup_proportion", default=0.1, type=float, help="The proportion of warmup.")
    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="The max value of grad norm.")
    parser.add_argument("--do_train", default=True, type=eval, help="Whether training.")
    parser.add_argument("--do_eval", default=True, type=eval, help="Whether evaluation.")
    parser.add_argument("--do_test", default=True, type=eval, help="Whether testing.")
    parser.add_argument("--device", type=str, default="gpu", help="Device for selecting for the training.")
    args = parser.METHOD_NAME()
    return args