code: def tokenize_sentences(self, untokenized_string: str):
          
          # load tokenizer
          assert isinstance(untokenized_string, str), \
              'Incoming argument must be a string.'
  
          if self.language == 'latin':
              self.models_path = self._get_models_path(self.language)
              try:
                  self.model = open_pickle(
                      os.path.expanduser(os.path.join(self.models_path, 'latin_punkt.pickle')))
              except FileNotFoundError as err:
                  raise type(err)(TokenizeSentence.missing_models_message + self.models_path)
              tokenizer = self.model
              tokenizer._lang_vars = self.lang_vars
          elif self.language == 'greek': # Workaround for regex tokenizer
              self.sent_end_chars=GreekLanguageVars.sent_end_chars
              self.sent_end_chars_regex = '|'.join(self.sent_end_chars)
              self.pattern = rf'(?<=[{self.sent_end_chars_regex}])\s'
          else:
              tokenizer = open_pickle(self.tokenizer_path)
              tokenizer = self._setup_tokenizer(tokenizer)
  
          # mk list of tokenized sentences
          if self.language == 'latin':
              return tokenizer.tokenize(untokenized_string)
          elif self.language == 'greek':
              return re.split(self.pattern, untokenized_string)
          else:
              tokenized_sentences = [sentence for sentence in
                                     tokenizer.sentences_from_text(untokenized_string,
                                                                   realign_boundaries=True)]
              return tokenized_sentences
docs: Tokenize sentences by reading trained tokenizer and invoking
          ``PunktSentenceTokenizer()``.
          :type untokenized_string: str
          :param untokenized_string: A string containing one of more sentences.
          :rtype : list of strings