code: def testLoss(self, agent_class):
      q_net = DummyNet(self._observation_spec, self._action_spec)
      agent = agent_class(
          self._time_step_spec,
          self._action_spec,
          q_network=q_net,
          optimizer=None,
      )
  
      observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      time_steps = ts.restart(observations, batch_size=2)
  
      actions = tf.constant([0, 1], dtype=tf.int32)
      action_steps = policy_step.PolicyStep(actions)
  
      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
      # [[1], [1]] from DummyNet above, we can calculate the following values:
      # Q-value for first observation/action pair: 2 * 1 + 1 * 2 + 1 = 5
      # Q-value for second observation/action pair: 1 * 3 + 1 * 4 + 1 = 8
      # (Here we use the second row of the kernel initializer above, since the
      # chosen action is now 1 instead of 0.)
      #
      # For target Q-values, action 0 produces a greater Q-value with a kernel of
      # [2, 1] instead of [1, 1] for action 1.
      # Target Q-value for first next_observation: 2 * 5 + 1 * 6 + 1 = 17
      # Target Q-value for second next_observation: 2 * 7 + 1 * 8 + 1 = 23
      # TD targets: 10 + 0.9 * 17 = 25.3 and 20 + 0.9 * 23 = 40.7
      # TD errors: 25.3 - 5 = 20.3 and 40.7 - 8 = 32.7
      # TD loss: 19.8 and 32.2 (Huber loss subtracts 0.5)
      # Overall loss: (19.8 + 32.2) / 2 = 26
      expected_loss = 26.0
      loss, _ = agent._loss(experience)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      self.assertAllClose(self.evaluate(loss), expected_loss)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import tensorflow
  from tf_agents.agents.dqn import dqn_agent
  from tf_agents.networks import network
  from tf_agents.networks import q_network
  from tf_agents.networks import sequential
  from tf_agents.networks import test_utils
  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
  from tf_agents.utils import test_utils
label_desc: Use image node_id 8 for calling the DqnAgentTest obj's underlying member method code with example usage: obj.testLoss(agent_class) without return types