code: def pad_stories(
      stories, sentence_length, max_story_length, pad_val=0.0
  ):
      """
      Pad all stories to have the same number of sentences (max_story_length).
      """
      nsamples = len(stories)
  
      X = (
          np.ones((nsamples, max_story_length, sentence_length))
          * pad_val
      ).astype(dtype=np.int32)
  
      for i, story in enumerate(stories):
          X[i, : len(story)] = story
  
      return X
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import itertools
  import os
  import pickle
  import sys
  import tarfile
  import numpy
  from tqdm import tqdm
  from nlp_architect.utils.generic import license_prompt
  from nlp_architect.utils.io import download_unlicensed_file, valid_path_append
label_desc: Use image node_id 2 for calling a global function with example usage: pad_stories(stories, sentence_length, max_story_length, pad_val) and returns: X