def METHOD_NAME(argv: Optional[List[str]] = None) -> None:
    """
    Handle command line arguments and gets things started.
    Parameters
    ----------
    argv : Optional[List[str]], default=None
        List of arguments, as if specified on the command-line.
        If ``None``, ``sys.argv[1:]`` is used instead.
    """
    # Get command line arguments
    parser = argparse.ArgumentParser(
        description="Takes an input feature file and removes any instances or"
        " features that do not match the specified patterns.",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )
    parser.add_argument(
        "-i",
        "--input",
        dest="infile",
        required=True,
        help="input feature file (ends in .arff, .csv, " ".jsonlines, .ndj, or .tsv)",
    )
    parser.add_argument(
        "-o",
        "--output",
        dest="outfile",
        required=True,
        help="output feature file (must have same extension " "as input file)",
    )
    parser.add_argument(
        "-f",
        "--feature",
        help="A feature in the feature file you would like to "
        "keep.  If unspecified, no features are removed.",
        nargs="*",
    )
    parser.add_argument(
        "-I",
        "--id",
        help="An instance ID in the feature file you would "
        "like to keep.  If unspecified, no instances are"
        " removed based on their IDs.",
        nargs="*",
    )
    parser.add_argument(
        "--id_col",
        help="Name of the column which contains the instance " "IDs in ARFF, CSV, or TSV files.",
        default="id",
    )
    parser.add_argument(
        "--inverse",
        help="Instead of keeping features and/or examples in " "lists, remove them.",
        action="store_true",
    )
    parser.add_argument(
        "-L",
        "--label",
        help="A label in the feature file you would like to "
        "keep.  If unspecified, no instances are removed"
        " based on their labels.",
        nargs="*",
    )
    parser.add_argument(
        "-l",
        "--label_col",
        help="Name of the column which contains the class "
        "labels in ARFF, CSV, or TSV files. For ARFF "
        "files, this must be the final column to count "
        "as the label.",
        default="y",
    )
    parser.add_argument(
        "-rb",
        "--replace_blanks_with",
        help="Specifies a new value with which to replace "
        "blank values in all columns in a CSV/TSV file. To "
        "replace blanks differently in each column, use "
        "the SKLL Reader API directly.",
        default=None,
    )
    parser.add_argument(
        "-db",
        "--drop_blanks",
        action="store_true",
        help="Drop all lines/rows in a CSV/TSV file that "
        "contain any blank values. If each row of the file "
        "contains a blank value, an exception will be raised.",
        default=False,
    )
    parser.add_argument(
        "-q", "--quiet", help='Suppress printing of "Loading..." messages.', action="store_true"
    )
    parser.add_argument("--version", action="version", version=f"%(prog)s {__version__}")
    args = parser.parse_args(argv)
    # Make warnings from built-in warnings module get formatted more nicely
    logging.captureWarnings(True)
    logging.basicConfig(format="%(asctime)s - %(name)s - %(levelname)s - " "%(message)s")
    logger = logging.getLogger(__name__)
    # all extensions except .libsvm can be processed
    valid_extensions = {ext for ext in EXT_TO_READER if ext != ".libsvm"}
    # make sure the input file extension is one we can process
    input_extension = Path(args.infile).suffix.lower()
    output_extension = Path(args.outfile).suffix.lower()
    if input_extension == ".libsvm":
        logger.error(
            "Cannot filter LibSVM files.  Please use skll_convert to"
            " convert to a different datatype first."
        )
        sys.exit(1)
    if input_extension not in valid_extensions:
        logger.error(
            "Input file must be in either .arff, .csv, .jsonlines, "
            ".ndj, or .tsv format. You specified: "
            f"{input_extension}"
        )
        sys.exit(1)
    if output_extension != input_extension:
        logger.error(
            "Output file must be in the same format as the input "
            f"file.  You specified: {output_extension}"
        )
        sys.exit(1)
    if input_extension == ".csv" or input_extension == ".tsv":
        replace_blanks_with = args.replace_blanks_with
        drop_blanks = args.drop_blanks
        if drop_blanks and replace_blanks_with is not None:
            raise ValueError(
                "You cannot both drop blanks and replace them. "
                "'replace_blanks_with' can only have a value "
                "when 'drop_blanks' is `False`."
            )
        replace_blanks_with = (
            None if replace_blanks_with is None else safe_float(replace_blanks_with)
        )
        kwargs = {"replace_blanks_with": replace_blanks_with, "drop_blanks": drop_blanks}
    else:
        kwargs = {}
    # Read input file
    reader = EXT_TO_READER[input_extension](
        args.infile, quiet=args.quiet, label_col=args.label_col, id_col=args.id_col, **kwargs
    )
    feature_set = reader.read()
    # convert the specified labels with `safe_float` before using
    # to match what the various featureset readers do
    converted_labels = None
    if args.label:
        converted_labels = [safe_float(label) for label in args.label]
    # Do the actual filtering
    feature_set.filter(
        ids=args.id, labels=converted_labels, features=args.feature, inverse=args.inverse
    )
    # write out the file in the requested output format
    writer_type = EXT_TO_WRITER[input_extension]
    writer_args = {"quiet": args.quiet}
    if writer_type is CSVWriter or writer_type is TSVWriter:
        writer_args["label_col"] = args.label_col
        writer_args["id_col"] = args.id_col
    elif writer_type is ARFFWriter:
        writer_args["label_col"] = args.label_col
        writer_args["id_col"] = args.id_col
        writer_args["regression"] = reader.regression
        writer_args["relation"] = reader.relation
    writer = writer_type(args.outfile, feature_set, **writer_args)
    writer.write()