class_docstr: This TokenInstance is a obect to have the basic units of data that should be
  extracted from the raw text file and can be consumed by any BERT like model.
class_docstr_tok: ["This","TokenInstance","is","a","obect","to","have","the","basic","units","of","data","that","should","be","extracted","from","the","raw","text","file","and","can","be","consumed","by","any","BERT","like","model","."]
code: def __init__(self, tokens_a, tokens_b, is_next, lang="en"):
      self.tokens_a = tokens_a
      self.tokens_b = tokens_b
      self.is_next = is_next  # 0 is if in continuation, 1 if is random
      self.lang = lang
  
imports: from tqdm import tqdm
  from typing import Tuple
  from random import shuffle
  import pickle
  import random
  import numpy
  from pathlib import Path
  from pytorch_pretrained_bert.tokenization import BertTokenizer
label_desc: Use image node_id 1 to create a new TokenInstance object  with example: obj = TokenInstance(tokens_a, tokens_b, is_next, lang)