class_docstr: This tokenizer is a wrapper for Jieba (https://github.com/fxsjy/jieba).
class_docstr_tok: ["This","tokenizer","is","a","wrapper","for","Jieba","(","https",":","\/\/github.com\/fxsjy\/jieba",")","."]
docstr: Tokenizes the text of the provided attribute of the incoming message.
docstr_tok: ["Tokenizes","the","text","of","the","provided","attribute","of","the","incoming","message","."]
code: def tokenize(self, message: Message, attribute: Text) -> List[Token]:
      """Tokenizes the text of the provided attribute of the incoming message."""
      import jieba
  
      text = message.get(attribute)
  
      tokenized = jieba.tokenize(text)
      tokens = [Token(word, start) for (word, start, end) in tokenized]
  
      return self._apply_token_pattern(tokens)
  
imports: from __future__ import annotations
  import glob
  import logging
  import os
  import shutil
  from typing import Any, Dict, List, Optional, Text
  from rasa.engine.graph import ExecutionContext
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.nlu.tokenizers.tokenizer import Token, Tokenizer
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.training_data.training_data import TrainingData
label_desc: Use image node_id 8 for calling the JiebaTokenizer obj's underlying member method code with example usage: obj.tokenize(message, attribute) and returns: self