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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 mscoco_generator(data_dir, tmp_dir, training, how_many, start_from=0, eos_list=None, vocab_filename=None):
 """Image generator for MSCOCO captioning problem with token-wise captions. Args: data_dir: path to the data directory. tmp_dir: path to temporary storage directory. training: a Boolean; if true, we use the train set, otherwise the test set. how_many: how many images and labels to generate. start_from: from which image to start. eos_list: optional list of end of sentence tokens, otherwise use default value `1`. vocab_filename: file within `tmp_dir` to read vocabulary from. Yields: A dictionary representing the images with the following fields: * image/encoded: the string encoding the image as JPEG, * image/format: the string "jpeg" representing image format, * image/class/label: a list of integers representing the caption, * image/height: an integer representing the height, * image/width: an integer representing the width. Every field is actually a list of the corresponding type. """

  eos_list = [1] if eos_list is None else eos_list
  def get_vocab():
    """Get vocab for caption text encoder."""
    if data_dir is not None and vocab_filename is not None:
      vocab_filepath = os.path.join(data_dir, vocab_filename)
      if tf.gfile.Exists(vocab_filepath):
        tf.logging.info("Found vocab file: %s", vocab_filepath)
        vocab_symbolizer = text_encoder.SubwordTextEncoder(vocab_filepath)
        return vocab_symbolizer
      else:
        raise ValueError("Vocab file does not exist: %s" % vocab_filepath)
    return None

  vocab_symbolizer = get_vocab()
  _get_mscoco(tmp_dir)
  caption_filepath = (
      _MSCOCO_TRAIN_CAPTION_FILE if training else _MSCOCO_EVAL_CAPTION_FILE)
  caption_filepath = os.path.join(tmp_dir, caption_filepath)
  prefix = _MSCOCO_TRAIN_PREFIX if training else _MSCOCO_EVAL_PREFIX
  caption_file = io.open(caption_filepath)
  caption_json = json.load(caption_file)
  # Dictionary from image_id to ((filename, height, width), captions).
  image_dict = {}
  for image in caption_json["images"]:
    image_dict[image["id"]] = [(image["file_name"], image["height"],
                                image["width"]), []]
  annotations = caption_json["annotations"]
  annotation_count = len(annotations)
  image_count = len(image_dict)
  tf.logging.info("Processing %d images and %d labels\n" % (image_count,
                                                            annotation_count))
  for annotation in annotations:
    image_id = annotation["image_id"]
    image_dict[image_id][1].append(annotation["caption"])

  data = list(image_dict.values())[start_from:start_from + how_many]
  random.shuffle(data)
  for image_info, labels in data:
    image_filename = image_info[0]
    image_filepath = os.path.join(tmp_dir, prefix, image_filename)
    with tf.gfile.Open(image_filepath, "rb") as f:
      encoded_image_data = f.read()
      height, width = image_info[1], image_info[2]
      for label in labels:
        if vocab_filename is None or vocab_symbolizer is None:
          label = [ord(c) for c in label] + eos_list
        else:
          label = vocab_symbolizer.encode(label) + eos_list
        yield {
            "image/encoded": [encoded_image_data],
            "image/format": ["jpeg"],
            "image/class/label": label,
            "image/height": [height],
            "image/width": [width]
        }