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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _generate_examples(tmp_dir, dataset_split):
 """Generate squad examples. Args: tmp_dir: a string dataset_split: problem.DatasetSplit.TRAIN or problem.DatasetSplit.EVAL Yields: dictionaries representing examples """

  if dataset_split == problem.DatasetSplit.TRAIN:
    file_name = _TRAINING_SET
  else:
    file_name = _DEV_SET
  squad_file = generator_utils.maybe_download(tmp_dir,
                                              file_name,
                                              os.path.join(_URL, file_name))
  with tf.gfile.GFile(squad_file, mode="r") as fp:
    squad = json.load(fp)

  version = squad["version"]
  for article in squad["data"]:
    if "title" in article:
      title = article["title"].strip()
    else:
      title = "no title"
    for paragraph in article["paragraphs"]:
      context = paragraph["context"].strip()
      for qa in paragraph["qas"]:
        question = qa["question"].strip()
        id_ = qa["id"]
        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
        answers = [answer["text"].strip() for answer in qa["answers"]]

        # Features currently used are "context", "question", and "answers".
        # Others are extracted here for the ease of future expansions.
        example = {
            "version": version,
            "title": title,
            "context": context,
            "question": question,
            "id": id_,
            "answer_starts": answer_starts,
            "answers": answers,
            "num_answers": len(answers),
            "is_supervised": True,
        }
        yield example