code: def testActorLoss(self):
      agent = td3_agent.Td3Agent(
          self._time_step_spec,
          self._action_spec,
          critic_network=self._critic_net,
          actor_network=self._unbounded_actor_net,
          actor_optimizer=None,
          critic_optimizer=None,
      )
  
      observations = [tf.constant([[1, 2], [3, 4]], dtype=tf.float32)]
      time_steps = ts.restart(observations, batch_size=2)
  
      actions = [2 * 1 + 1 * 2 + 5, 2 * 3 + 1 * 4 + 5]
      negative_q_values = [
          -(1 * 1 + 3 * 2 + 2 * actions[0] + 4),
          -(1 * 3 + 3 * 4 + 2 * actions[1] + 4),
      ]
      expected_loss = np.mean(negative_q_values)
      loss = agent.actor_loss(time_steps)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      loss_ = self.evaluate(loss)
      self.assertAllClose(loss_, expected_loss)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  from tf_agents.agents.td3 import td3_agent
  from tf_agents.networks import network
  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 test_utils
label_desc: Use image node_id 6 for calling the TD3AgentTest obj's underlying member method code with example usage: obj.testActorLoss() without return types