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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 _wiki_articles(shard_id, wikis_dir=None):
 """Generates WikipediaArticles from GCS that are part of shard shard_id."""

  if not wikis_dir:
    wikis_dir = WIKI_CONTENT_DIR
  with tf.Graph().as_default():
    dataset = tf.data.TFRecordDataset(
        cc_utils.readahead(
            os.path.join(wikis_dir, WIKI_CONTENT_FILE % shard_id)),
        buffer_size=16 * 1000 * 1000)

    def _parse_example(ex_ser):
      """Parse serialized Example containing Wikipedia article content."""
      features = {
          "url": tf.VarLenFeature(tf.string),
          "title": tf.VarLenFeature(tf.string),
          "section_titles": tf.VarLenFeature(tf.string),
          "section_texts": tf.VarLenFeature(tf.string),
      }
      ex = tf.parse_single_example(ex_ser, features)
      for k in ex.keys():
        ex[k] = ex[k].values
      ex["url"] = ex["url"][0]
      ex["title"] = ex["title"][0]
      return ex

    dataset = dataset.map(_parse_example, num_parallel_calls=32)
    dataset = dataset.prefetch(100)
    record_it = dataset.make_one_shot_iterator().get_next()

    with tf.Session() as sess:
      while True:
        try:
          ex = sess.run(record_it)
        except tf.errors.OutOfRangeError:
          break

        sections = [
            WikipediaSection(title=text_encoder.to_unicode(title),
                             text=text_encoder.to_unicode(text))
            for title, text in zip(ex["section_titles"], ex["section_texts"])
        ]
        yield WikipediaArticle(
            url=text_encoder.to_unicode(ex["url"]),
            title=text_encoder.to_unicode(ex["title"]),
            sections=sections)