code: def testLoss(self, mock_actions_and_log_probs, mock_apply_gradients):
      # Mock _actions_and_log_probs so that _train() and _loss() run on the same
      # sampled values.
      actions = tf.constant([[0.2], [0.5], [-0.8]])
      log_pi = tf.constant([-1.1, -0.8, -0.5])
      mock_actions_and_log_probs.return_value = (actions, log_pi)
  
      # Skip applying gradients since mocking _actions_and_log_probs.
      del mock_apply_gradients
  
      actor_net = actor_distribution_network.ActorDistributionNetwork(
          self._obs_spec,
          self._action_spec,
          fc_layer_params=(10,),
          continuous_projection_net=tanh_normal_projection_network.TanhNormalProjectionNetwork,
      )
  
      agent = sac_agent.SacAgent(
          self._time_step_spec,
          self._action_spec,
          critic_network=DummyCriticNet(),
          actor_network=actor_net,
          actor_optimizer=tf.compat.v1.train.AdamOptimizer(0.001),
          critic_optimizer=tf.compat.v1.train.AdamOptimizer(0.001),
          alpha_optimizer=tf.compat.v1.train.AdamOptimizer(0.001),
      )
  
      observations = tf.constant(
          [[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]],
          dtype=tf.float32,
      )
      actions = tf.constant(
          [[[0], [1]], [[2], [3]], [[4], [5]]], dtype=tf.float32
      )
      time_steps = ts.TimeStep(
          step_type=tf.constant([[1, 1]] * 3, dtype=tf.int32),
          reward=tf.constant([[1, 1]] * 3, dtype=tf.float32),
          discount=tf.constant([[1, 1]] * 3, dtype=tf.float32),
          observation=observations,
      )
  
      experience = trajectory.Trajectory(
          time_steps.step_type,
          observations,
          actions,
          (),
          time_steps.step_type,
          time_steps.reward,
          time_steps.discount,
      )
  
      test_util.test_loss_and_train_output(
          test=self,
          expect_equal_loss_values=True,
          agent=agent,
          experience=experience,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import mock
  import tensorflow
  from tf_agents.agents import test_util
  from tf_agents.agents.ddpg import critic_rnn_network
  from tf_agents.agents.sac import sac_agent
  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 nest_map
  from tf_agents.networks import network
  from tf_agents.networks import sequential
  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 test_utils
label_desc: Use image node_id 9 for calling the SacAgentTest obj's underlying member method code with example usage: obj.testLoss(mock_actions_and_log_probs, mock_apply_gradients) without return types