docs: def distorted_inputs(data_dir, batch_size):
    """Construct distorted input for CIFAR training using the Reader ops.
  
    Args:
      data_dir: Path to the CIFAR-10 data directory.
      batch_size: Number of images per batch.
  
    Returns:
      images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.
    """
    filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
                 for i in xrange(1, 6)]
    for f in filenames:
      if not tf.gfile.Exists(f):
        raise ValueError('Failed to find file: ' + f)
  
    # Create a queue that produces the filenames to read.
    filename_queue = tf.train.string_input_producer(filenames)
  
    # Read examples from files in the filename queue.
    read_input = read_cifar10(filename_queue)
    reshaped_image = tf.cast(read_input.uint8image, tf.float32)
  
    height = IMAGE_SIZE
    width = IMAGE_SIZE
  
    # Image processing for training the network. Note the many random
    # distortions applied to the image.
  
    # Randomly crop a [height, width] section of the image.
    distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
  
    # Randomly flip the image horizontally.
    distorted_image = tf.image.random_flip_left_right(distorted_image)
  
    # Because these operations are not commutative, consider randomizing
    # the order their operation.
    distorted_image = tf.image.random_brightness(distorted_image,
                                                 max_delta=63)
    distorted_image = tf.image.random_contrast(distorted_image,
                                               lower=0.2, upper=1.8)
  
    # Subtract off the mean and divide by the variance of the pixels.
    float_image = tf.image.per_image_standardization(distorted_image)
  
    # Set the shapes of tensors.
    float_image.set_shape([height, width, 3])
    read_input.label.set_shape([1])
  
    # Ensure that the random shuffling has good mixing properties.
    min_fraction_of_examples_in_queue = 0.4
    min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
                             min_fraction_of_examples_in_queue)
    print ('Filling queue with %d CIFAR images before starting to train. '
           'This will take a few minutes.' % min_queue_examples)
  
    # Generate a batch of images and labels by building up a queue of examples.
    return _generate_image_and_label_batch(float_image, read_input.label,
                                           min_queue_examples, batch_size,
                                           shuffle=True)