code: def testTrainAgentHeteroscedastic(self):
      optimizer = tf.compat.v1.train.GradientDescentOptimizer(
          learning_rate=0.1
      )
      agent = (
          dropout_thompson_sampling_agent.DropoutThompsonSamplingAgent(
              self._time_step_spec,
              self._action_spec,
              optimizer=optimizer,
              dropout_rate=0.1,
              network_layers=(20, 20, 20),
              dropout_only_top_layer=False,
              heteroscedastic=True,
          )
      )
      observations = np.array([[1, 2], [3, 4]], dtype=np.float32)
      actions = np.array([0, 1], dtype=np.int32)
      rewards = np.array([0.5, 3.0], dtype=np.float32)
      initial_step, final_step = _get_initial_and_final_steps(
          observations, rewards
      )
      action_step = _get_action_step(actions)
      experience = _get_experience(
          initial_step, action_step, final_step
      )
      loss_before, _ = agent.train(experience, None)
      loss_after, _ = agent.train(experience, None)
      self.evaluate(tf.compat.v1.global_variables_initializer())
      self.assertIsNotNone(self.evaluate(loss_before))
      self.assertIsNotNone(self.evaluate(loss_after))
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  from tf_agents.bandits.agents import dropout_thompson_sampling_agent
  from tf_agents.bandits.drivers import driver_utils
  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
label_desc: Use image node_id 5 for calling the AgentTest obj's underlying member method code with example usage: obj.testTrainAgentHeteroscedastic() without return types