How to use the datasets.dataset_utils.write_label_file function in datasets

'label': slim.tfexample_decoder.Tensor('image/class/label'),
      'label_text': slim.tfexample_decoder.Tensor('image/class/text'),
      'object/bbox': slim.tfexample_decoder.BoundingBox(
          ['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'),
      'object/label': slim.tfexample_decoder.Tensor('image/object/class/label'),
  }

  decoder = slim.tfexample_decoder.TFExampleDecoder(
      keys_to_features, items_to_handlers)

  labels_to_names = None
  if dataset_utils.has_labels(dataset_dir):
    labels_to_names = dataset_utils.read_label_file(dataset_dir)
  else:
    labels_to_names = create_readable_names_for_imagenet_labels()
    dataset_utils.write_label_file(labels_to_names, dataset_dir)

  return slim.dataset.Dataset(
      data_sources=file_pattern,
      reader=reader,
      decoder=decoder,
      num_samples=_SPLITS_TO_SIZES[split_name],
      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
      num_classes=_NUM_CLASSES,
      labels_to_names=labels_to_names)

# Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Flowers dataset!')

# Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Flowers dataset!')

_convert_dataset_with_text('train', training_filenames, class_names_to_ids,
                   dataset_dir, df_dict, tfrecords_subdir)
  _convert_dataset_with_text('validation', validation_filenames, class_names_to_ids,
                   dataset_dir, df_dict, tfrecords_subdir)

  # Write the train/validation split size
  train_valid_split = dict(zip(['train', 'validation'], [len(photo_filenames) - num_valid, num_valid]))
  train_valid_filename = os.path.join(dataset_dir, photos_subdir, _TRAIN_VALID_FILENAME)
  with tf.gfile.Open(train_valid_filename, 'w') as f:
    for split_name in train_valid_split:
      size = train_valid_split[split_name]
      f.write('%s:%d\n' % (split_name, size))

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir, photos_subdir)

  #_clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the dataset!')

# Divide into train and test:
    random.seed(_RANDOM_SEED)
    random.shuffle(photo_filenames)
    training_filenames = photo_filenames[_NUM_VALIDATION:]
    validation_filenames = photo_filenames[:_NUM_VALIDATION]

    # First, convert the training and validation sets.
    _convert_dataset('train', training_filenames, class_names_to_ids,
                     dataset_dir)
    _convert_dataset('validation', validation_filenames, class_names_to_ids,
                     dataset_dir)

    # Finally, write the labels file:
    labels_to_class_names = dict(zip(range(len(class_names)), class_names))
    dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

    _clean_up_temporary_files(dataset_dir)
    print('\nFinished converting the Flowers dataset!')

# Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  _NUM_VALIDATION = int(len(photo_filenames) * _PERCENT_VALIDATION)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   FLAGS.dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   FLAGS.dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, FLAGS.dataset_dir)

  print('\nFinished converting the Arts dataset!')

# First, process the training data:
    with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
        data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
        labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
        _add_to_tfrecord(data_filename, labels_filename, 60000, tfrecord_writer)

    # Next, process the testing data:
    with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
        data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
        labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
        _add_to_tfrecord(data_filename, labels_filename, 10000, tfrecord_writer)

    # Finally, write the labels file:
    labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
    dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

    _clean_up_temporary_files(dataset_dir)
    print('\nFinished converting the MNIST dataset!')

for i in range(_NUM_TRAIN_FILES):
      filename = os.path.join(dataset_dir,
                              'cifar-10-batches-py',
                              'data_batch_%d' % (i + 1))  # 1-indexed.
      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    filename = os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'test_batch')
    _add_to_tfrecord(filename, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  #_clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')

filenames.append(os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'data_batch_%d' % (i + 1)))  # 1-indexed.
  _add_to_tfrecord(filenames, 'train', dataset_dir)

  # Next, process the testing data:
  #with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
  filenames = []
  filenames.append( os.path.join(dataset_dir,
                          'cifar-10-batches-py',
                          'test_batch'))
  _add_to_tfrecord(filenames, 'test', dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')