code: def encode_for_summarization_new_tokenizer(
      story_lines, summary_lines, tokenizer
  ):
      """Encode the story and summary lines, and join them
      as specified in [1] by using `[SEP] [CLS]` tokens to separate
      sentences.
      """
      story_lines_token_ids = [
          tokenizer.encode(line).ids for line in story_lines
      ]
      story_token_ids = [
          token
          for sentence in story_lines_token_ids
          for token in sentence
      ]
      summary_lines_token_ids = [
          tokenizer.encode(line).ids for line in summary_lines
      ]
      summary_token_ids = [
          token
          for sentence in summary_lines_token_ids
          for token in sentence
      ]
  
      return story_token_ids, summary_token_ids
  
imports: import re
  import html
  import logging
  import pandas
  import os
  import random
  import torch
  from pathlib import Path
  import pickle
  import shutil
  from collections import deque, namedtuple
  from torch.utils.data import Dataset, DataLoader, SequentialSampler
  from tokenizers import BertWordPieceTokenizer
  from transformers import BertTokenizer
label_desc: Use image node_id 5 for calling a global function with example usage: encode_for_summarization_new_tokenizer(story_lines, summary_lines, tokenizer) and returns: story_token_ids,  summary_token_ids