code: def augment(image, label):
      """Placeholder for data augmentation."""
      with tf.variable_scope("augmentations"):
          num_images_ = tf.shape(image)[0]
          # random rotate
          angle = 15.0
          processed_data = tf.contrib.image.rotate(
              image,
              tf.random_uniform(
                  [num_images_],
                  maxval=math.pi / 180 * angle,
                  minval=math.pi / 180 * -angle,
              ),
          )
          # random shift
          rank = 2
          max_pad = 4
          min_pad = 0
          random_pad = tf.random_uniform(
              shape=(rank, 2),
              minval=min_pad,
              maxval=max_pad + 1,
              dtype=tf.int32,
          )
          random_pad = tf.pad(random_pad, paddings=[[0, 1], [0, 0]])
          image = tf.pad(image, random_pad)
          slice_begin = random_pad[:, 1]
          slice_end = [28, 28, 1]
          image = tf.slice(
              image, slice_begin, slice_end, name="translate"
          )
  
      return image, label
  
file_path: attributionpriors/attributionpriors/mnist/mnist_reader.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import argparse
  import os.path
  import sys
  import time
  import math
  import tensorflow
  from tensorflow.examples.tutorials.mnist import mnist
label_desc: Use image node_id 2 for calling a global function with example usage: augment(image, label) and returns: image,  label