code: def _babi_parser(
      tmp_dir, babi_task_id, subset, dataset_split, joint_training=True
  ):
      """Parsing the bAbi dataset (train and test).
  
      Args:
        tmp_dir: temp directory to download and extract the dataset
        babi_task_id: babi task id
        subset: babi subset
        dataset_split: dataset split (train or eval)
        joint_training: if training the model on all tasks.
  
      Returns:
         babi_instances: set of training examples, each a dict containing a story,
         a question and an answer.
         babi_lines: all the texts in the data separated based on their
         appearance in the stories, questions, or answers.
      """
  
      def _data_file(mode, task_id):
          """Generates the path to the data file for the given mode(train/test).
  
          Args:
            mode: either train or test for bAbi dataset
            task_id: babi task id
  
          Returns:
            data file path
          """
          file_name = _TASKS[task_id] + "_{}.txt"
          return os.path.join(_DIR_NAME, subset, file_name.format(mode))
  
      def _all_task_raw_data_generator(
          tmp_dir, data_file, dataset_split
      ):
          """Prepares raw data for all tasks to gether..
  
          Args:
            tmp_dir: temp directory
            data_file: data file
            dataset_split: dataset split
          """
  
          tf.logging.info("Preparing dataset of all task together")
          globe_name = "*_{}.txt"
          mode_name = "test"
          if dataset_split == problem.DatasetSplit.TRAIN:
              mode_name = "train"
          files_name = os.path.join(
              tmp_dir, _DIR_NAME, subset, globe_name.format(mode_name)
          )
          with tf.gfile.GFile(data_file, "wb") as outfile:
              for filename in tf.gfile.Glob(files_name):
                  if filename == data_file:
                      # don"t want to copy the output into the output
                      continue
                  with tf.gfile.GFile(filename, "rb") as readfile:
                      shutil.copyfileobj(readfile, outfile)
  
      def _parse_answer(answer):
          if joint_training or babi_task_id in [
              "qa8",
              "qa19",
              "qa0",
          ]:  # "lists-sets" or "path finding"
              return "".join(
                  [d for d in answer.split(",")]
              )  # as a single token!
          else:
              return answer
  
      if dataset_split == problem.DatasetSplit.TRAIN:
          babi_train_task_id = "qa0" if joint_training else babi_task_id
          data_file = os.path.join(
              tmp_dir, _data_file("train", babi_train_task_id)
          )
      else:
          data_file = os.path.join(
              tmp_dir, _data_file("test", babi_task_id)
          )
  
      if (
          babi_task_id == "qa0" or joint_training
      ) and not tf.gfile.Exists(os.path.join(tmp_dir, data_file)):
          _all_task_raw_data_generator(
              tmp_dir, data_file, dataset_split
          )
  
      tf.logging.info(
          "Parsing %s into training/testing instances...", data_file
      )
  
      babi_instances = []
      with tf.gfile.GFile(data_file, mode="r") as f:
          story = []
          for line in f:
              line_num, line = line.strip().split(" ", 1)
              if int(line_num) == 1:
                  story = []
              if "\t" in line:
                  question, answer, _ = line.split("\t")
                  question = _normalize_string(question)
                  substories = [s for s in story if s]
                  answer = _parse_answer(answer)
                  instance = {
                      FeatureNames.STORY: substories,
                      FeatureNames.QUESTION: question,
                      FeatureNames.ANSWER: answer,
                  }
                  babi_instances.append(instance)
  
                  story.append("")
              else:
                  story.append(_normalize_string(line))
  
      return babi_instances
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import os
  import shutil
  import tarfile
  import requests
  import six
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.data_generators import tokenizer
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import metrics
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling a global function with example usage: _babi_parser(tmp_dir, babi_task_id, subset, dataset_split, joint_training) and returns: babi_instances, os, str, answer