code: def read_data(finish_fast=False):
      # Get the file
      try:
          path = get_file(
              "babi-tasks-v1-2.tar.gz",
              origin="https://s3.amazonaws.com/text-datasets/"
              "babi_tasks_1-20_v1-2.tar.gz",
          )
      except Exception:
          print(
              "Error downloading dataset, please download it manually:\n"
              "$ wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2"  # noqa: E501
              ".tar.gz\n"
              "$ mv tasks_1-20_v1-2.tar.gz ~/.keras/datasets/babi-tasks-v1-2.tar.gz"  # noqa: E501
          )
          raise
  
      # Choose challenge
      challenges = {
          # QA1 with 10,000 samples
          "single_supporting_fact_10k": "tasks_1-20_v1-2/en-10k/qa1_"
          "single-supporting-fact_{}.txt",
          # QA2 with 10,000 samples
          "two_supporting_facts_10k": "tasks_1-20_v1-2/en-10k/qa2_"
          "two-supporting-facts_{}.txt",
      }
      challenge_type = "single_supporting_fact_10k"
      challenge = challenges[challenge_type]
  
      with tarfile.open(path) as tar:
          train_stories = get_stories(
              tar.extractfile(challenge.format("train"))
          )
          test_stories = get_stories(
              tar.extractfile(challenge.format("test"))
          )
      if finish_fast:
          train_stories = train_stories[:64]
          test_stories = test_stories[:64]
      return train_stories, test_stories
  
imports: from __future__ import print_function
  from tensorflow.keras.models import Sequential, Model, load_model
  from tensorflow.keras.layers import Embedding
  from tensorflow.keras.layers import Input, Activation, Dense, Permute, Dropout
  from tensorflow.keras.layers import add, dot, concatenate
  from tensorflow.keras.layers import LSTM
  from tensorflow.keras.optimizers import RMSprop
  from tensorflow.keras.utils import get_file
  from tensorflow.keras.preprocessing.sequence import pad_sequences
  from filelock import FileLock
  import os
  import argparse
  import tarfile
  import numpy
  import re
  from ray import train, tune
label_desc: Use image node_id 5 for calling a global function with example usage: read_data(finish_fast) and returns: train_stories,  test_stories