code: def encode_for_summarization(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) 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) 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
  
file_path: transformers/examples/research_projects/bertabs/utils_summarization.py
imports: import os
  from collections import deque
  import torch
  from torch.utils.data import Dataset
label_desc: Use image node_id 5 for calling a global function with example usage: encode_for_summarization(story_lines, summary_lines, tokenizer) and returns: story_token_ids,  summary_token_ids