code: def create_masked_lm_predictions(
      tokens, masked_lm_prob, max_predictions_per_seq, vocab_words
  ):
      """Creates the predictions for the masked LM objective."""
  
      cand_indexes = []
      for i, token in enumerate(tokens):
          if token == "[CLS]" or token == "[SEP]":
              continue
          cand_indexes.append(i)
  
      random.shuffle(cand_indexes)
  
      output_tokens = list(tokens)
  
      num_to_predict = min(
          max_predictions_per_seq,
          max(1, int(round(len(tokens) * masked_lm_prob))),
      )
  
      masked_lms = []
      covered_indexes = set()
      for index in cand_indexes:
          if len(masked_lms) >= num_to_predict:
              break
          if index in covered_indexes:
              continue
          covered_indexes.add(index)
  
          masked_token = None
          # 80% of the time, replace with [MASK]
          if random.random() < 0.8:
              masked_token = "[MASK]"
          else:
              # 10% of the time, keep original
              if random.random() < 0.5:
                  masked_token = tokens[index]
              # 10% of the time, replace with random word
              else:
                  masked_token = vocab_words[
                      random.randint(0, len(vocab_words) - 1)
                  ]
  
          output_tokens[index] = masked_token
  
          masked_lms.append(
              MaskedLmInstance(index=index, label=tokens[index])
          )
  
      masked_lms = sorted(masked_lms, key=lambda x: x.index)
  
      masked_lm_positions = []
      masked_lm_labels = []
      for p in masked_lms:
          masked_lm_positions.append(p.index)
          masked_lm_labels.append(p.label)
  
      return (output_tokens, masked_lm_positions, masked_lm_labels)
  
imports: import collections
  import csv
  import os
  import zipfile
  import requests
  import tensorflow
  from adversarial_filtering.bert import modeling
  from adversarial_filtering.bert import tokenization
  import random
  import gcsfs
  import numpy
  from tensorflow.python.lib.io import file_io
label_desc: Use image node_id 5 for calling a global function with example usage: create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_words) and returns: output_tokens,  masked_lm_positions,  masked_lm_labels