def select_and_apply_random_policy()

in autoaugment.py [0:0]

• 8 lines of code
• 4 McCabe index (conditional complexity)

def select_and_apply_random_policy(policies, image):
  """Select a random policy from `policies` and apply it to `image`."""
  policy_to_select = tf.random_uniform([], maxval=len(policies), dtype=tf.int32)
  # Note that using tf.case instead of tf.conds would result in significantly
  # larger graphs and would even break export for some larger policies.
  for (i, policy) in enumerate(policies):
    image = tf.cond(
        tf.equal(i, policy_to_select),
        lambda selected_policy=policy: selected_policy(image),
        lambda: image)
  return image