class_docstr: Constructs a BERT tokenizer. Based on WordPiece.
  Args:
      vocab_file (:obj:`string`):
          File containing the vocabulary.
      do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
          Whether to lowercase the input when tokenizing.
      do_basic_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`):
          Whether to do basic tokenization before WordPiece.
      never_split (:obj:`bool`, `optional`, defaults to :obj:`True`):
          List of tokens which will never be split during tokenization. Only has an effect when
          :obj:`do_basic_tokenize=True`
      unk_token (:obj:`string`, `optional`, defaults to '[UNK]'):
          The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
          token instead.
      sep_token (:obj:`string`, `optional`, defaults to '[SEP]'):
          The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences
          for sequence classification or for a text and a question for question answering.
          It is also used as the last token of a sequence built with special tokens.
      pad_token (:obj:`string`, `optional`, defaults to '[PAD]'):
          The token used for padding, for example when batching sequences of different lengths.
      cls_token (:obj:`string`, `optional`, defaults to '[CLS]'):
          The classifier token which is used when doing sequence classification (classification of the whole
          sequence instead of per-token classification). It is the first token of the sequence when built with
          special tokens.
      mask_token (:obj:`string`, `optional`, defaults to '[MASK]'):
          The token used for masking values. This is the token used when training this model with masked language
          modeling. This is the token which the model will try to predict.
      tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
          Whether to tokenize Chinese characters.
          This should likely be deactivated for Japanese:
          see: https://github.com/huggingface/transformers/issues/328
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code: def vocab_size(self):
      return len(self.vocab)
  
imports: import collections
  import os
  import pickle
  import unicodedata
  from typing import Dict, List, Optional, Union, Tuple
  from paddle.utils import try_import
  from paddlehub.text.utils import load_vocab, is_whitespace, is_control, is_punctuation, whitespace_tokenize, is_chinese_char
label_desc: Use image node_id 2 for calling the BertTokenizer obj's underlying member method code with example usage: obj.vocab_size() and returns: len