class_docstr: BERT model
class_docstr_tok: ["BERT","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 BertTokenizer.from_pretrained(
          pretrained_model_name_or_path="bert-large-uncased",
          *args,
          **kwargs,
      )
  
file_path: PaddleHub/modules/text/language_model/bert-large-uncased/module.py
imports: from typing import Dict
  import os
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddlenlp.transformers.bert.modeling import BertForSequenceClassification, BertModel, BertForTokenClassification
  from paddlenlp.transformers.bert.tokenizer import BertTokenizer
  from paddlenlp.metrics import ChunkEvaluator
  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 Bert obj's underlying member method code with example usage: obj.get_tokenizer() and returns: BertTokenizer