How to use the sudachipy.tokenizer.Tokenizer.SplitMode function in SudachiPy

nlp = load_model(model_path)
    if disable_pipes:
        print("disabling pipes: {}".format(disable_pipes), file=sys.stderr)
        nlp.disable_pipes(disable_pipes)
        print("using : {}".format(nlp.pipe_names), file=sys.stderr)
    else:
        # to ensure reflect local changes of corrector
        if recreate_corrector and 'JapaneseCorrector' in nlp.pipe_names:
            nlp.remove_pipe('JapaneseCorrector')
            corrector = JapaneseCorrector(nlp)
            nlp.add_pipe(corrector, last=True)

    if mode == 'A':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.A
    elif mode == 'B':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.B
    elif mode == 'C':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.C
    else:
        raise Exception('mode should be A, B or C')
    print("mode is {}".format(mode), file=sys.stderr)
    if not use_sentence_separator:
        print("disabling sentence separator", file=sys.stderr)
        nlp.tokenizer.use_sentence_separator = False

    if output_path:
        output = open(str(output_path), 'w')
    else:
        output = sys.stdout

    line = ''
    try:

msg += "\n  2. make sure dictionary is successfully installed."
            raise ImportError(msg)

        super(SudachiTokenizer, self).__init__(
            name="sudachi ({})".format(mode), with_postag=with_postag,
        )
        try:
            self._tokenizer = dictionary.Dictionary().create()
        except KeyError:
            msg = "Loading a dictionary fails."
            msg += " ( see https://github.com/WorksApplications/SudachiPy#install-dict-packages )"  # NOQA
            raise KeyError(msg)

        _mode = mode.capitalize()
        if _mode == "A":
            self._mode = tokenizer.Tokenizer.SplitMode.A
        elif _mode == "B":
            self._mode = tokenizer.Tokenizer.SplitMode.B
        elif _mode == "C":
            self._mode = tokenizer.Tokenizer.SplitMode.C
        else:
            raise ValueError("Invalid mode is specified. Mode should be A, B, or C.")  # NOQA

if model_path:
        nlp = spacy.load(model_path)
    else:
        nlp = spacy.load('ja_ginza')
    if disable_pipes:
        print("disabling pipes: {}".format(disable_pipes), file=sys.stderr)
        nlp.disable_pipes(disable_pipes)
        print("using : {}".format(nlp.pipe_names), file=sys.stderr)
    if recreate_corrector:
        if 'JapaneseCorrector' in nlp.pipe_names:
            nlp.remove_pipe('JapaneseCorrector')
        corrector = JapaneseCorrector(nlp)
        nlp.add_pipe(corrector, last=True)

    if mode == 'A':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.A
    elif mode == 'B':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.B
    elif mode == 'C':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.C
    else:
        raise Exception('mode should be A, B or C')
    print("mode is {}".format(mode), file=sys.stderr)
    if not use_sentence_separator:
        print("disabling sentence separator", file=sys.stderr)
        nlp.tokenizer.use_sentence_separator = False

    if output_path:
        output = open(str(output_path), 'w')
    else:
        output = sys.stdout

def _command_tokenize(args, print_usage):
    if args.version:
        print_version()
        return

    _input_files_checker(args, print_usage)

    if args.mode == "A":
        mode = tokenizer.Tokenizer.SplitMode.A
    elif args.mode == "B":
        mode = tokenizer.Tokenizer.SplitMode.B
    else:
        mode = tokenizer.Tokenizer.SplitMode.C

    stdout_logger = logging.getLogger(__name__)
    output = sys.stdout
    if args.fpath_out:
        output = open(args.fpath_out, "w", encoding="utf-8")
    handler = logging.StreamHandler(output)
    handler.setLevel(logging.DEBUG)
    stdout_logger.addHandler(handler)
    stdout_logger.setLevel(logging.DEBUG)
    stdout_logger.propagate = False

    print_all = args.a

print("disabling pipes: {}".format(disable_pipes), file=sys.stderr)
        nlp.disable_pipes(disable_pipes)
        print("using : {}".format(nlp.pipe_names), file=sys.stderr)
    else:
        # to ensure reflect local changes of corrector
        if recreate_corrector and 'JapaneseCorrector' in nlp.pipe_names:
            nlp.remove_pipe('JapaneseCorrector')
            corrector = JapaneseCorrector(nlp)
            nlp.add_pipe(corrector, last=True)

    if mode == 'A':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.A
    elif mode == 'B':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.B
    elif mode == 'C':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.C
    else:
        raise Exception('mode should be A, B or C')
    print("mode is {}".format(mode), file=sys.stderr)
    if not use_sentence_separator:
        print("disabling sentence separator", file=sys.stderr)
        nlp.tokenizer.use_sentence_separator = False

    if browser_command:
        browser = webbrowser.get(browser_command)
    else:
        browser = None

    if corpus_type:
        if corpus_type == 'bccwj_ud':
            doc = correct_dep(convert_files(lines)[0].to_doc(nlp.vocab, True), False)
            print('Displaying first sentence with result and raw_result:', doc.text, file=sys.stderr)

spacy.require_gpu()
        print("GPU enabled", file=sys.stderr)
    nlp = load_model(model_path)
    if disable_pipes:
        print("disabling pipes: {}".format(disable_pipes), file=sys.stderr)
        nlp.disable_pipes(disable_pipes)
        print("using : {}".format(nlp.pipe_names), file=sys.stderr)
    else:
        # to ensure reflect local changes of corrector
        if recreate_corrector and 'JapaneseCorrector' in nlp.pipe_names:
            nlp.remove_pipe('JapaneseCorrector')
            corrector = JapaneseCorrector(nlp)
            nlp.add_pipe(corrector, last=True)

    if mode == 'A':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.A
    elif mode == 'B':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.B
    elif mode == 'C':
        nlp.tokenizer.mode = OriginalTokenizer.SplitMode.C
    else:
        raise Exception('mode should be A, B or C')
    print("mode is {}".format(mode), file=sys.stderr)
    if not use_sentence_separator:
        print("disabling sentence separator", file=sys.stderr)
        nlp.tokenizer.use_sentence_separator = False

    if output_path:
        output = open(str(output_path), 'w')
    else:
        output = sys.stdout