def METHOD_NAME(parser: ArgumentParser):
    """
    Register this command to argparse so it's available for the transformer-cli
    :param parser: Root parser to register command-specific arguments
    :return:
    """
    train_parser = parser.add_parser("train", help="CLI tool to train a model on a task.")
    train_parser.add_argument(
        "--train_data",
        type=str,
        required=True,
        help="path to train (and optionally evaluation) dataset as a csv with "
        "tab separated labels and sentences.",
    )
    train_parser.add_argument(
        "--column_label", type=int, default=0, help="Column of the dataset csv file with example labels."
    )
    train_parser.add_argument(
        "--column_text", type=int, default=1, help="Column of the dataset csv file with example texts."
    )
    train_parser.add_argument(
        "--column_id", type=int, default=2, help="Column of the dataset csv file with example ids."
    )
    train_parser.add_argument(
        "--skip_first_row", action="store_true", help="Skip the first row of the csv file (headers)."
    )
    train_parser.add_argument("--validation_data", type=str, default="", help="path to validation dataset.")
    train_parser.add_argument(
        "--validation_split",
        type=float,
        default=0.1,
        help="if validation dataset is not provided, fraction of train dataset " "to use as validation dataset.",
    )
    train_parser.add_argument("--output", type=str, default="./", help="path to saved the trained model.")
    train_parser.add_argument(
        "--task", type=str, default="text_classification", help="Task to train the model on."
    )
    train_parser.add_argument(
        "--model", type=str, default="bert-base-uncased", help="Model's name or path to stored model."
    )
    train_parser.add_argument("--train_batch_size", type=int, default=32, help="Batch size for training.")
    train_parser.add_argument("--valid_batch_size", type=int, default=64, help="Batch size for validation.")
    train_parser.add_argument("--learning_rate", type=float, default=3e-5, help="Learning rate.")
    train_parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon for Adam optimizer.")
    train_parser.set_defaults(func=train_command_factory)