code: 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
  
imports: import dataclasses
  import inspect
  import math
  import tensorflow.compat.v1
  from tensorflow_addons import image
label_desc: Use image node_id 31 for calling a global function with example usage: select_and_apply_random_policy(policies, image) and returns: image