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: Loads a custom dictionary from model storage.
docstr_tok: ["Loads","a","custom","dictionary","from","model","storage","."]
code: def load(
      cls,
      config: Dict[Text, Any],
      model_storage: ModelStorage,
      resource: Resource,
      execution_context: ExecutionContext,
      **kwargs: Any,
  ) -> JiebaTokenizer:
      """Loads a custom dictionary from model storage."""
      dictionary_path = config["dictionary_path"]
  
      # If a custom dictionary path is in the config we know that it should have
      # been saved to the model storage.
      if dictionary_path is not None:
          try:
              with model_storage.read_from(
                  resource
              ) as resource_directory:
                  cls._load_custom_dictionary(str(resource_directory))
          except ValueError:
              logger.debug(
                  f"Failed to load {cls.__name__} from model storage. "
                  f"Resource '{resource.name}' doesn't exist."
              )
      return cls(config, model_storage, resource)
  
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 9 for calling the JiebaTokenizer obj's underlying member method code with example usage: obj.load(cls, config, model_storage, resource, execution_context) and returns: cls