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: Load all the custom dictionaries stored in the path.
  
  More information about the dictionaries file format can
  be found in the documentation of jieba.
  https://github.com/fxsjy/jieba#load-dictionary
docstr_tok: ["Load","all","the","custom","dictionaries","stored","in","the","path",".","More","information","about","the","dictionaries","file","format","can","be","found","in","the","documentation","of","jieba",".","https",":","\/\/github.com\/fxsjy\/jieba","#","load-dictionary"]
code: def _load_custom_dictionary(path: Text) -> None:
      """Load all the custom dictionaries stored in the path.
  
      More information about the dictionaries file format can
      be found in the documentation of jieba.
      https://github.com/fxsjy/jieba#load-dictionary
      """
      import jieba
  
      jieba_userdicts = glob.glob(f"{path}/*")
      for jieba_userdict in jieba_userdicts:
          logger.info(
              f"Loading Jieba User Dictionary at {jieba_userdict}"
          )
          jieba.load_userdict(jieba_userdict)
  
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 6 for calling the JiebaTokenizer obj's underlying member method code with example usage: obj._load_custom_dictionary(path) without return types