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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def random_shift(image, wsr=0.1, hsr=0.1):
 """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. """

  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)