import tensorflow as tf

    A tuple (images, labels), where:
    * images is a float tensor with shape [batch_size, FLAGS.num_scales, FLAGS.crop_size, FLAGS.crop_size]
    * labels is an int32 tensor with shape [batch_size] with the true label,
      a number in the range [0, NUM_CLASSES()).
    Note that an tf.train.QueueRunner is added to the graph, which
    must be run using e.g. tf.train.start_queue_runners().
  """
  if not num_epochs: num_epochs = None
  filename = os.path.join(FLAGS.train_dir, 'data',
                          '{}_{}.tfrecords'.format(name, records.tfrecord_name()))

  with tf.name_scope('input'):
    filename_queue = tf.train.string_input_producer(
        [filename], num_epochs=num_epochs)

    # Even when reading in multiple threads, share the filename
    # queue.
    image, label = read_and_decode(filename_queue)

    # Shuffle the examples and collect them into batch_size batches.
    # (Internally uses a RandomShuffleQueue.)
    # We run this in two threads to avoid being a bottleneck.
