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

probabilities = tf.nn.softmax(logits)

    checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
    init_fn = slim.assign_from_checkpoint_fn(checkpoint_path, slim.get_variables_to_restore())

    with tf.Session() as sess:
      with slim.queues.QueueRunners(sess):
        sess.run(tf.initialize_local_variables())
        init_fn(sess)
        np_images, np_probabilities = sess.run([image, probabilities])
        predicted_label = np.argmax(np_probabilities[0, :])
        print(predicted_label)

    labels_to_names = None
    if dataset_utils.has_labels(FLAGS.dataset_dir):
      labels_to_names = dataset_utils.read_label_file(FLAGS.dataset_dir)

    class_name = labels_to_names[predicted_label]
    print(class_name)

    if dataset_utils.has_labels(FLAGS.dataset_dir, INFO_FILE):
      class_names_to_info = read_info_file(FLAGS.dataset_dir)

    if class_name in merge_info:
      print('It is the back of a coin, please take a photo of front.')
      class_name_list = merge_info[class_name]
      for item in class_name_list:
        info = class_names_to_info[item.split('_')[0]]
        print('Maybe the value is {}'.format(info['value']))
    else:
      name_info = class_name.split('_')
      origin_name = name_info[0]

items_to_handlers = {
      'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'),
      '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 LOAD_READABLE_NAMES:
    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)

'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),
      'image/class/label': tf.FixedLenFeature(
          [], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
  }

  items_to_handlers = {
      'image': slim.tfexample_decoder.Image(),
      'label': slim.tfexample_decoder.Tensor('image/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)

  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)

items_to_handlers = {
        'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'),
        '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)

'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),
        'image/class/label': tf.FixedLenFeature(
            [], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
    }

    items_to_handlers = {
        'image': slim.tfexample_decoder.Image(),
        'label': slim.tfexample_decoder.Tensor('image/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)

    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)

}
    items_to_handlers = {
        'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'),
        'shape': slim.tfexample_decoder.Tensor('image/shape'),
        'object/bbox': slim.tfexample_decoder.BoundingBox(
                ['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'),
        'object/label': slim.tfexample_decoder.Tensor('image/object/bbox/label'),
        'object/difficult': slim.tfexample_decoder.Tensor('image/object/bbox/difficult'),
        'object/truncated': slim.tfexample_decoder.Tensor('image/object/bbox/truncated'),
    }
    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=split_to_sizes[split_name],
            items_to_descriptions=items_to_descriptions,
            num_classes=num_classes,
            labels_to_names=labels_to_names)

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

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

  labels_to_names = None
  labels_file = os.path.join(dataset_dir, LABELS_FILENAME)
  if tf.gfile.Exists(labels_file):
    labels_to_names = dataset_utils.read_label_file(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)

'image/format': tf.FixedLenFeature((), tf.string, default_value='png'),
      'image/class/label': tf.FixedLenFeature(
          [], tf.int64, default_value=tf.zeros([], dtype=tf.int64)),
  }

  items_to_handlers = {
      'image': slim.tfexample_decoder.Image(),
      'label': slim.tfexample_decoder.Tensor('image/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)

  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)

'image/format': tf.FixedLenFeature((), tf.string, default_value='raw'),
      'image/class/label': tf.FixedLenFeature(
          [1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)),
  }

  items_to_handlers = {
      'image': slim.tfexample_decoder.Image(shape=[28, 28, 1], channels=1),
      'label': slim.tfexample_decoder.Tensor('image/class/label', shape=[]),
  }

  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)

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