class_docstr: RoBERTa model
class_docstr_tok: ["RoBERTa","model"]
docstr: Gets the tokenizer that is customized for this module.
docstr_tok: ["Gets","the","tokenizer","that","is","customized","for","this","module","."]
code: def get_tokenizer(*args, **kwargs):
      """
      Gets the tokenizer that is customized for this module.
      """
      return AutoTokenizer.from_pretrained(
          pretrained_model_name_or_path="hfl/roberta-wwm-ext-large",
          *args,
          **kwargs,
      )
  
file_path: PaddleHub/modules/text/language_model/roberta-wwm-ext-large/module.py
imports: import math
  import os
  from typing import Dict
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddlenlp.metrics import ChunkEvaluator
  from paddlenlp.transformers import AutoModel
  from paddlenlp.transformers import AutoModelForSequenceClassification
  from paddlenlp.transformers import AutoModelForTokenClassification
  from paddlenlp.transformers import AutoTokenizer
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.nlp_module import TransformerModule
  from paddlehub.utils.log import logger
label_desc: Use image node_id 3 for calling the Roberta obj's underlying member method code with example usage: obj.get_tokenizer() and returns: AutoTokenizer