docstr: Tokenizes a list of documents using a BERT tokenizer
  
  Args:
      text (list): List of strings (one sequence) or
          tuples (two sequences).
  
  Returns:
      [list]: List of lists. Each sublist contains WordPiece tokens
          of the input sequence(s).
docstr_tok: ["Tokenizes","a","list","of","documents","using","a","BERT","tokenizer","Args",":","text","(","list",")",":","List","of","strings","(","one","sequence",")","or","tuples","(","two","sequences",")",".","Returns",":","[","list","]",":","List","of","lists",".","Each","sublist","contains","WordPiece","tokens","of","the","input","sequence","(","s",")","."]
code: def tokenize(self, text):
      """Tokenizes a list of documents using a BERT tokenizer
  
      Args:
          text (list): List of strings (one sequence) or
              tuples (two sequences).
  
      Returns:
          [list]: List of lists. Each sublist contains WordPiece tokens
              of the input sequence(s).
      """
      if isinstance(text[0], str):
          return [self.tokenizer.tokenize(x) for x in tqdm(text)]
      else:
          return [
              [self.tokenizer.tokenize(x) for x in sentences]
              for sentences in tqdm(text)
          ]
  
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 2 for calling the Tokenizer obj's underlying member method code with example usage: obj.tokenize(text) and returns: unknown, unknown