class_docstr: Word piece tokenizer for BERT models.
class_docstr_tok: ["Word","piece","tokenizer","for","BERT","models","."]
code: def __init__(
      self, wordpiece_vocab, basic_tokenizer, wordpiece_tokenizer
  ) -> None:
      self.vocab = wordpiece_vocab
      self.basic_tokenizer = basic_tokenizer
      self.wordpiece_tokenizer = wordpiece_tokenizer
      log_class_usage(__class__)
  
imports: import copy
  import json
  import re
  from collections import OrderedDict
  from typing import List, NamedTuple, Optional, Union
  import torch
  from fairseq.data.encoders.gpt2_bpe import get_encoder
  from fairseq.data.encoders.gpt2_bpe_utils import Encoder
  from pytext.config import ConfigBase
  from pytext.config.component import Component, ComponentType, create_component
  from pytext.data.pickleable_gpt2bpe_encoder import PickleableGPT2BPEEncoder
  from pytext.torchscript.tokenizer import ScriptDoNothingTokenizer, ScriptWordTokenizer
  from pytext.utils.file_io import PathManager
  from pytext.utils.usage import log_class_usage
  from transformers.tokenization_bert import BasicTokenizer, WordpieceTokenizer
label_desc: Use image node_id 1 to create a new WordPieceTokenizer object from inherited base classes: Tokenizer with example: obj = WordPieceTokenizer(wordpiece_vocab, basic_tokenizer, wordpiece_tokenizer)