code: def exp3_update_value(
      reward: types.Float, log_prob: types.Float
  ) -> types.Float:
      return 1.0 - (1.0 - reward) / tf.exp(log_prob)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Optional, Text
  import gin
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import tf_agent
  from tf_agents.bandits.policies import categorical_policy
  from tf_agents.policies import utils
  from tf_agents.trajectories import policy_step
  from tf_agents.typing import types
  from tf_agents.utils import common
label_desc: Use image node_id 2 for calling a global function with example usage: exp3_update_value(reward, log_prob) and returns: unknown