docs: def inputs(dataset, batch_size=None, num_preprocess_threads=None):
    """Generate batches of ImageNet images for evaluation.
  
    Use this function as the inputs for evaluating a network.
  
    Note that some (minimal) image preprocessing occurs during evaluation
    including central cropping and resizing of the image to fit the network.
  
    Args:
      dataset: instance of Dataset class specifying the dataset.
      batch_size: integer, number of examples in batch
      num_preprocess_threads: integer, total number of preprocessing threads but
        None defaults to FLAGS.num_preprocess_threads.
  
    Returns:
      images: Images. 4D tensor of size [batch_size, FLAGS.image_size,
                                         image_size, 3].
      labels: 1-D integer Tensor of [FLAGS.batch_size].
    """
    if not batch_size:
      batch_size = FLAGS.batch_size
  
    # Force all input processing onto CPU in order to reserve the GPU for
    # the forward inference and back-propagation.
    with tf.device('/cpu:0'):
      images, labels = batch_inputs(
          dataset, batch_size, train=False,
          num_preprocess_threads=num_preprocess_threads,
          num_readers=1)
  
    return images, labels