code: def testAgentWithDifferentSubagentsUpdate(self):
      num_actions = 3
      context_dim = 2
      batch_size = 7
      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
      )
      agent1 = lin_ucb_agent.LinearUCBAgent(
          time_step_spec,
          action_spec,
          emit_policy_info=(
              policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN,
          ),
      )
      reward_net = q_network.QNetwork(
          input_tensor_spec=observation_spec,
          action_spec=action_spec,
          fc_layer_params=(4, 3, 2),
      )
      agent2 = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent(
          time_step_spec,
          action_spec,
          reward_network=reward_net,
          emit_policy_info=(
              policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN,
          ),
          optimizer=tf.compat.v1.train.GradientDescentOptimizer(
              learning_rate=0.1
          ),
          epsilon=0.1,
      )
      agents = [agent1, agent2]
      dist = tfd.Categorical(probs=tf.Variable([0.0, 1.0]))
      mixed_agent = WeightRotatingMixtureAgent(dist, 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, 2, num_actions)
      experience = _get_experience(
          initial_step, action_step, final_step
      )
      self.evaluate(mixed_agent.initialize())
      loss_info = mixed_agent.train(experience)
      self.evaluate(loss_info)
  
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 lin_ucb_agent
  from tf_agents.bandits.agents import mixture_agent
  from tf_agents.bandits.agents import neural_epsilon_greedy_agent
  from tf_agents.bandits.drivers import driver_utils
  from tf_agents.bandits.policies import mixture_policy
  from tf_agents.networks import q_network
  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 4 for calling the MixtureAgentTest obj's underlying member method code with example usage: obj.testAgentWithDifferentSubagentsUpdate() without return types