code: def testWithAdvantageFn(self, with_value_network):
      advantage_fn = mock.Mock(side_effect=lambda returns, _: returns)
  
      value_network = (
          DummyValueNet(self._obs_spec) if with_value_network else None
      )
      agent = reinforce_agent.ReinforceAgent(
          self._time_step_spec,
          self._action_spec,
          actor_network=DummyActorNet(
              self._obs_spec, self._action_spec, unbounded_actions=False
          ),
          value_network=value_network,
          advantage_fn=advantage_fn,
          optimizer=None,
      )
  
      step_type = tf.constant(
          [
              [
                  ts.StepType.FIRST,
                  ts.StepType.LAST,
                  ts.StepType.FIRST,
                  ts.StepType.LAST,
              ]
          ]
      )
      next_step_type = tf.constant(
          [
              [
                  ts.StepType.LAST,
                  ts.StepType.FIRST,
                  ts.StepType.LAST,
                  ts.StepType.FIRST,
              ]
          ]
      )
      reward = tf.constant([[0, 0, 0, 0]], dtype=tf.float32)
      discount = tf.constant([[1, 1, 1, 1]], dtype=tf.float32)
      observations = tf.constant(
          [[[1, 2], [1, 2], [1, 2], [1, 2]]], dtype=tf.float32
      )
      actions = tf.constant([[[0], [1], [2], [3]]], dtype=tf.float32)
  
      experience = trajectory.Trajectory(
          step_type,
          observations,
          actions,
          (),
          next_step_type,
          reward,
          discount,
      )
  
      agent.total_loss(experience, reward, None)
  
      advantage_fn.assert_called_once()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  from absl.testing.absltest import mock
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents.reinforce import reinforce_agent
  from tf_agents.networks import actor_distribution_rnn_network
  from tf_agents.networks import network
  from tf_agents.networks 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.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 23 for calling the ReinforceAgentTest obj's underlying member method code with example usage: obj.testWithAdvantageFn(with_value_network) without return types