docstr: Initializes the underlying pretrained BERT tokenizer.
  
  Args:
      language (Language, optional): The pretrained model's language.
                                     Defaults to Language.ENGLISH.
      cache_dir (str, optional): Location of BERT's cache directory.
          Defaults to ".".
docstr_tok: ["Initializes","the","underlying","pretrained","BERT","tokenizer",".","Args",":","language","(","Language",",","optional",")",":","The","pretrained","model","'s","language",".","Defaults","to","Language.ENGLISH",".","cache_dir","(","str",",","optional",")",":","Location","of","BERT","'s","cache","directory",".","Defaults","to","``",".","``","."]
code: def __init__(
      self, language=Language.ENGLISH, to_lower=False, cache_dir="."
  ):
      """Initializes the underlying pretrained BERT tokenizer.
  
      Args:
          language (Language, optional): The pretrained model's language.
                                         Defaults to Language.ENGLISH.
          cache_dir (str, optional): Location of BERT's cache directory.
              Defaults to ".".
      """
      self.tokenizer = BertTokenizer.from_pretrained(
          language, do_lower_case=to_lower, cache_dir=cache_dir
      )
      self.language = language
  
imports: import logging
  import warnings
  from collections.abc import Iterable
  from enum import Enum
  import torch
  from pytorch_pretrained_bert.tokenization import BertTokenizer
  from tqdm import tqdm
  import numpy
  import torch.nn
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
  from pytorch_pretrained_bert.modeling import BertForSequenceClassification
  from pytorch_pretrained_bert.optimization import BertAdam
  from interpret_text.experimental.common.utils_pytorch import get_device, move_to_device
  from interpret_text.experimental.common.constants import BertTokens
  from collections import namedtuple
  from cached_property import cached_property
label_desc: Use image node_id 1 to create a new Tokenizer object  with example: obj = Tokenizer(language, to_lower, cache_dir)