code: def testLoss(self):
      q_net = DummyNet((self._observation_spec, self._action_spec))
      agent = qtopt_agent.QtOptAgent(
          self._time_step_spec,
          self._action_spec,
          q_network=q_net,
          optimizer=None,
          init_mean_cem=self._mean,
          init_var_cem=self._var,
          num_samples_cem=self._num_samples,
          actions_sampler=self._sampler,
      )
  
      agent._target_q_network_delayed = DummyNet(
          (self._observation_spec, self._action_spec), bias=1
      )
  
      observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      time_steps = ts.restart(observations, batch_size=2)
  
      actions = tf.constant([[0.0], [0.0]], dtype=tf.float32)
      action_steps = policy_step.PolicyStep(actions, info=())
  
      rewards = tf.constant([10, 20], dtype=tf.float32)
      discounts = tf.constant([0.9, 0.9], dtype=tf.float32)
      next_observations = tf.constant(
          [[5, 6], [7, 8]], dtype=tf.float32
      )
      next_time_steps = ts.transition(
          next_observations, rewards, discounts
      )
  
      experience = (
          trajectories_test_utils.stacked_trajectory_from_transition(
              time_steps, action_steps, next_time_steps
          )
      )
  
      # Using the kernel initializer [[2, 1], [1, 1]] and bias initializer
      # ([[2], [2]] for q_network/target_network, [[1], [1]] for delayed
      # target_network)
      # from DummyNet above, we can calculate the following values:
      # Q Network:
      # Q-value for first observation/action pair: 2 * 1 + 1 * 2 + 2 = 6
      # Q-value for second observation/action pair: 2 * 3 + 1 * 4 + 2 = 12
      # Target Network:
      # Q-value for first next_observation: 2 * 5 + 1 * 6 + 2 = 18
      # Q-value for second next_observation: 2 * 7 + 1 * 8 + 2 = 24
      # Delayed Target Network:
      # Q-value for first next_observation: 2 * 5 + 1 * 6 + 1 = 17
      # Q-value for second next_observation: 2 * 7 + 1 * 8 + 1 = 23
      # TD targets: 10 + 0.9 * min(17, 18) = 25.3; 20 + 0.9 * min(23, 24) = 40.7
      # TD errors: 25.3 - 6 = 19.3; 40.7 - 12 = 28.7
      # TD loss: 18.8 and 28.2 (Huber loss subtracts 0.5)
      # Overall loss: (18.8 + 28.2) / 2 = 23.5
      expected_td_loss = 23.5
      loss, loss_info = agent._loss(experience)
  
      self.evaluate(tf.compat.v1.initialize_all_variables())
      self.assertAllClose(self.evaluate(loss), expected_td_loss)
      self.assertAllClose(
          self.evaluate(tf.reduce_mean(loss_info.td_loss)),
          expected_td_loss,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.agents.ddpg import critic_network
  from tf_agents.agents.qtopt import qtopt_agent
  from tf_agents.networks import network
  from tf_agents.policies.samplers import qtopt_cem_actions_sampler_continuous
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import test_utils
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
label_desc: Use image node_id 5 for calling the QtoptAgentTest obj's underlying member method code with example usage: obj.testLoss() without return types