code: def testCriticLoss(
      self,
      include_critic_entropy_term,
      reward_noise_variance,
      use_tf_variable,
      td_targets,
  ):
      if use_tf_variable:
          reward_noise_variance = tf.Variable(reward_noise_variance)
      agent = cql_sac_agent.CqlSacAgent(
          self._time_step_spec,
          self._action_spec,
          critic_network=DummyCriticNet(),
          actor_network=None,
          actor_optimizer=None,
          critic_optimizer=None,
          alpha_optimizer=None,
          cql_alpha=1.0,
          num_cql_samples=1,
          include_critic_entropy_term=include_critic_entropy_term,
          use_lagrange_cql_alpha=False,
          reward_noise_variance=reward_noise_variance,
          actor_policy_ctor=DummyActorPolicy,
      )
  
      observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      time_steps = ts.restart(observations, batch_size=2)
      actions = tf.constant([[5], [6]], dtype=tf.float32)
  
      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
      )
  
      pred_td_targets = [7.0, 10.0]
      self.evaluate(tf.compat.v1.global_variables_initializer())
  
      # Expected critic loss has factor of 2, for the two TD3 critics.
      expected_loss = self.evaluate(
          2
          * tf.compat.v1.losses.mean_squared_error(
              tf.constant(td_targets), tf.constant(pred_td_targets)
          )
      )
  
      loss = agent._critic_loss_with_optional_entropy_term(
          time_steps,
          actions,
          next_time_steps,
          td_errors_loss_fn=tf.math.squared_difference,
      )
  
      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
  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 5 for calling the CqlSacAgentTest obj's underlying member method code with example usage: obj.testCriticLoss(include_critic_entropy_term, reward_noise_variance, use_tf_variable, td_targets) without return types