def METHOD_NAME(argv: Optional[List[str]] = None) -> None:
    """
    Handle command line arguments and get 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.
        Defaults to ``None``.
    """
    parser = argparse.ArgumentParser(
        description="Prints out the weights of a" " given model.",
        conflict_handler="resolve",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )
    parser.add_argument("model_file", help="model file to load")
    group = parser.add_mutually_exclusive_group()
    group.add_argument(
        "--k", help="number of top features to print (0 for all)", type=int, default=50
    )
    group.add_argument(
        "--sort_by_labels",
        "-s",
        action="store_true",
        default=False,
        help="order the features by classes",
    )
    parser.add_argument(
        "--sign",
        choices=["positive", "negative", "all"],
        default="all",
        help="show only positive, only negative or all weights",
    )
    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")
    k = args.k if args.k > 0 else None
    learner = Learner.from_file(args.model_file)
    (weights, intercept) = learner.model_params
    multiclass = False
    model = learner._model
    if (
        isinstance(model, LinearSVC)
        or (isinstance(model, LogisticRegression) and len(learner.label_list) > 2)
        or (isinstance(model, SVC) and model.kernel == "linear")
    ):
        multiclass = True
    weight_items = iter(weights.items())
    if args.sign == "positive":
        weight_items = (x for x in weight_items if x[1] > 0)
    elif args.sign == "negative":
        weight_items = (x for x in weight_items if x[1] < 0)
    if intercept is not None:
        # subclass of LinearModel
        if "_intercept_" in intercept:
            # Some learners (e.g. LinearSVR) may return an array of intercepts but
            # sometimes that array is of length 1 so we don't need to print that
            # as an array/list. First, let's normalize these cases.
            model_intercepts = intercept["_intercept_"]
            intercept_is_array = isinstance(model_intercepts, np.ndarray)
            num_intercepts = len(model_intercepts) if intercept_is_array else 1
            if intercept_is_array and num_intercepts == 1:
                model_intercepts = model_intercepts[0]
                intercept_is_array = False
            # now print out the intercepts
            print(f"intercept = {model_intercepts:.12f}")
        else:
            print("== intercept values ==")
            for label, val in intercept.items():
                print(f"{val:.12f}\t{label}")
        print()
    print("Number of nonzero features:", len(weights), file=sys.stderr)
    weight_by_class: DefaultDict[str, Dict[str, float]] = defaultdict(dict)
    if multiclass and args.sort_by_labels:
        for label_feature, weight in weight_items:
            label, feature = label_feature.split()
            weight_by_class[label][feature] = weight
        for label in sorted(weight_by_class):
            for feat, val in sorted(weight_by_class[label].items(), key=lambda x: -abs(x[1])):
                print(f"{val:.12f}\t{label}\t{feat}")
    else:
        for feat, val in sorted(weight_items, key=lambda x: -abs(x[1]))[:k]:
            print(f"{val:.12f}\t{feat}")