code: def testTrainWithRnn(
      self,
      cql_alpha,
      num_cql_samples,
      include_critic_entropy_term,
      use_lagrange_cql_alpha,
      expected_loss,
  ):
      actor_net = (
          actor_distribution_rnn_network.ActorDistributionRnnNetwork(
              self._obs_spec,
              self._action_spec,
              input_fc_layer_params=None,
              output_fc_layer_params=None,
              conv_layer_params=None,
              lstm_size=(40,),
          )
      )
  
      critic_net = critic_rnn_network.CriticRnnNetwork(
          (self._obs_spec, self._action_spec),
          observation_fc_layer_params=(16,),
          action_fc_layer_params=(16,),
          joint_fc_layer_params=(16,),
          lstm_size=(16,),
          output_fc_layer_params=None,
      )
  
      counter = common.create_variable("test_train_counter")
  
      optimizer_fn = tf.compat.v1.train.AdamOptimizer
  
      agent = cql_sac_agent.CqlSacAgent(
          self._time_step_spec,
          self._action_spec,
          critic_network=critic_net,
          actor_network=actor_net,
          actor_optimizer=optimizer_fn(1e-3),
          critic_optimizer=optimizer_fn(1e-3),
          alpha_optimizer=optimizer_fn(1e-3),
          cql_alpha=cql_alpha,
          num_cql_samples=num_cql_samples,
          include_critic_entropy_term=include_critic_entropy_term,
          use_lagrange_cql_alpha=use_lagrange_cql_alpha,
          random_seed=self._random_seed,
          train_step_counter=counter,
      )
  
      batch_size = 5
      observations = tf.constant(
          [[[1, 2], [3, 4], [5, 6]]] * batch_size, dtype=tf.float32
      )
      actions = tf.constant(
          [[[0], [1], [1]]] * batch_size, dtype=tf.float32
      )
      time_steps = ts.TimeStep(
          step_type=tf.constant([[1] * 3] * batch_size, dtype=tf.int32),
          reward=tf.constant([[1] * 3] * batch_size, dtype=tf.float32),
          discount=tf.constant(
              [[1] * 3] * batch_size, dtype=tf.float32
          ),
          observation=observations,
      )
  
      experience = trajectory.Trajectory(
          time_steps.step_type,
          observations,
          actions,
          (),
          time_steps.step_type,
          time_steps.reward,
          time_steps.discount,
      )
  
      # Force variable creation.
      agent.policy.variables()
  
      if not tf.executing_eagerly():
          # Get experience first to make sure optimizer variables are created and
          # can be initialized.
          experience = agent.train(experience)
          with self.cached_session() as sess:
              common.initialize_uninitialized_variables(sess)
          self.assertEqual(self.evaluate(counter), 0)
          self.evaluate(experience)
          self.assertEqual(self.evaluate(counter), 1)
      else:
          self.assertEqual(self.evaluate(counter), 0)
          loss = self.evaluate(agent.train(experience))
          self.assertAllClose(loss.loss, expected_loss)
          self.assertEqual(self.evaluate(counter), 1)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents.cql import cql_sac_agent
  from tf_agents.agents.ddpg import critic_network
  from tf_agents.agents.ddpg import critic_rnn_network
  from tf_agents.agents.sac import tanh_normal_projection_network
  from tf_agents.networks import actor_distribution_network
  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 distribution_spec
  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
  from tf_agents.utils import test_utils
label_desc: Use image node_id 7 for calling the CqlSacAgentTest obj's underlying member method code with example usage: obj.testTrainWithRnn(cql_alpha, num_cql_samples, include_critic_entropy_term, use_lagrange_cql_alpha, expected_loss) without return types