code: def testLossRNNSmokeTest(self, agent_class):
      q_net = sequential.Sequential(
          [
              tf.keras.layers.LSTM(
                  2,
                  return_state=True,
                  return_sequences=True,
                  kernel_initializer=tf.constant_initializer(0.5),
                  recurrent_initializer=tf.constant_initializer(0.5),
              ),
          ]
      )
  
      agent = agent_class(
          self._time_step_spec,
          self._action_spec,
          q_network=q_net,
          gamma=0.95,
          optimizer=None,
      )
  
      observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      time_steps = ts.restart(observations, batch_size=2)
  
      rewards = tf.constant([10, 20], dtype=tf.float32)
      discounts = tf.constant([0.7, 0.8], dtype=tf.float32)
  
      next_observations = tf.constant(
          [[5, 6], [7, 8]], dtype=tf.float32
      )
      next_time_steps = ts.transition(
          next_observations, rewards, discounts
      )
      third_observations = tf.constant(
          [[9, 10], [11, 12]], dtype=tf.float32
      )
      third_time_steps = ts.transition(
          third_observations, rewards, discounts
      )
  
      actions = tf.constant([0, 1], dtype=tf.int32)
      action_steps = policy_step.PolicyStep(actions)
  
      experience1 = trajectory.from_transition(
          time_steps, action_steps, next_time_steps
      )
      experience2 = trajectory.from_transition(
          next_time_steps, action_steps, third_time_steps
      )
      experience3 = trajectory.from_transition(
          third_time_steps, action_steps, third_time_steps
      )
  
      experience = tf.nest.map_structure(
          lambda x, y, z: tf.stack([x, y, z], axis=1),
          experience1,
          experience2,
          experience3,
      )
  
      loss, _ = agent._loss(experience)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
  
      # Smoke test, here to make sure the calculation does not change as we
      # modify preprocessing or other internals.
      expected_loss = 28.722265
      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 12 for calling the DqnAgentTest obj's underlying member method code with example usage: obj.testLossRNNSmokeTest(agent_class) without return types