code: def random_shift(image, wsr=0.1, hsr=0.1):
    
    height, width, _ = common_layers.shape_list(image)
    width_range, height_range = wsr*width, hsr*height
    height_translations = tf.random_uniform((1,), -height_range, height_range)
    width_translations = tf.random_uniform((1,), -width_range, width_range)
    translations = tf.concat((height_translations, width_translations), axis=0)
    return tf.contrib.image.translate(image, translations=translations)
docs: Apply random horizontal and vertical shift to images.
  
    This is the default data-augmentation strategy used on CIFAR in Glow.
  
    Args:
      image: a 3-D Tensor
      wsr: Width shift range, as a float fraction of the width.
      hsr: Height shift range, as a float fraction of the width.
    Returns:
      images: images translated by the provided wsr and hsr.