code: def testMixtureUpdate(
      self, batch_size, context_dim, num_agents, emit_policy_info
  ):
      num_actions = 5
      observation_spec = tensor_spec.TensorSpec(
          [context_dim], tf.float32
      )
      time_step_spec = time_step.time_step_spec(observation_spec)
      action_spec = tensor_spec.BoundedTensorSpec(
          dtype=tf.int32, shape=(), minimum=0, maximum=num_actions - 1
      )
      agents = []
      policy_info = (
          (policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN,)
          if emit_policy_info
          else ()
      )
      for _ in range(num_agents):
          agents.append(
              lin_ucb_agent.LinearUCBAgent(
                  time_step_spec,
                  action_spec,
                  emit_policy_info=policy_info,
              )
          )
      mixed_agent = exp3_mixture_agent.Exp3MixtureAgent(agents)
      initial_step, final_step = _get_initial_and_final_steps(
          batch_size, context_dim
      )
      action = np.random.randint(
          num_actions, size=batch_size, dtype=np.int32
      )
      action_step = _get_action_step(
          action, num_agents, num_actions, emit_policy_info
      )
      if emit_policy_info:
          self.assertEqual(
              self.evaluate(
                  action_step.info[
                      "subpolicy_info"
                  ].predicted_rewards_mean[0, 0]
              ),
              0,
          )
      experience = _get_experience(
          initial_step, action_step, final_step
      )
      self.evaluate(mixed_agent.initialize())
      self.evaluate(mixed_agent._variable_collection.reward_aggregates)
      self.evaluate(mixed_agent.train(experience))
      reward_aggregates = self.evaluate(
          mixed_agent._variable_collection.reward_aggregates
      )
      self.assertAllInSet(reward_aggregates[: num_agents - 1], [0.999])
      agent_prob = 1 / num_agents
      est_rewards = 0.5 / agent_prob
      per_step_update = est_rewards
      last_agent_update = 1 - batch_size * per_step_update
      self.assertAllClose(
          reward_aggregates[-1], last_agent_update * 0.999
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.agents import exp3_mixture_agent
  from tf_agents.bandits.agents import lin_ucb_agent
  from tf_agents.bandits.drivers import driver_utils
  from tf_agents.bandits.policies import mixture_policy
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
label_desc: Use image node_id 3 for calling the Exp3MixtureAgentTest obj's underlying member method code with example usage: obj.testMixtureUpdate(batch_size, context_dim, num_agents, emit_policy_info) without return types