class_docstr: Preprocessor for a BERT-based classifier.
  
  Attributes:
    seq_len: Length of the input sequence to the model.
    vocab_file: File containing the BERT vocab.
    tokenizer: BERT tokenizer.
    model_name: Name of the model provided by the model_spec. Used to associate
      cached files with specific Bert model vocab.
    preprocessor: Which preprocessor to use. Must be one of the enum values of
      SupportedBertPreprocessors.
class_docstr_tok: ["Preprocessor","for","a","BERT-based","classifier",".","Attributes",":","seq_len",":","Length","of","the","input","sequence","to","the","model",".","vocab_file",":","File","containing","the","BERT","vocab",".","tokenizer",":","BERT","tokenizer",".","model_name",":","Name","of","the","model","provided","by","the","model_spec",".","Used","to","associate","cached","files","with","specific","Bert","model","vocab",".","preprocessor",":","Which","preprocessor","to","use",".","Must","be","one","of","the","enum","values","of","SupportedBertPreprocessors","."]
code: def tokenizer(self) -> bert_tokenizer.BertTokenizer:
      return self._tokenizer
  
imports: import collections
  import hashlib
  import os
  import re
  from typing import Mapping, Sequence, Tuple, Union
  import tensorflow
  import tensorflow_hub
  from mediapipe.model_maker.python.core.data import cache_files
  from mediapipe.model_maker.python.text.text_classifier import bert_tokenizer
  from mediapipe.model_maker.python.text.text_classifier import dataset
label_desc: Use image node_id 6 for calling the BertClassifierPreprocessor obj's underlying member method code with example usage: obj.tokenizer() and returns: self