code: def testBatchedActorLoss(self):
      class FakeCriticNetwork(network.Network):
          """Fake critic network with random output Q value of a specified shape."""
  
          def __init__(
              self, input_tensor_spec, output_shape, name=None
          ):
              self._output_shape = output_shape
              super(FakeCriticNetwork, self).__init__(
                  input_tensor_spec, state_spec=(), name=name
              )
  
          def call(self, inputs, step_type=None, network_state=()):
              q_value = tf.random.uniform(
                  self._output_shape,
                  minval=0,
                  maxval=1,
                  dtype=tf.dtypes.float32,
              )
              return q_value, network_state
  
      critic_input_spec = (self._obs_spec, self._action_spec)
      critic_net = FakeCriticNetwork(
          critic_input_spec,
          output_shape=(2, 1),
      )
      obs_spec = [
          tensor_spec.BoundedTensorSpec(
              [1], tf.float32, minimum=0, maximum=1
          )
      ]
      time_step_spec = ts.time_step_spec(obs_spec)
      agent = td3_agent.Td3Agent(
          time_step_spec,
          self._action_spec,
          critic_network=critic_net,
          actor_network=self._unbounded_actor_net,
          actor_optimizer=None,
          critic_optimizer=None,
      )
  
      observations = [tf.constant([[1, 2], [3, 4]], dtype=tf.float32)]
      time_steps = ts.restart(observations, batch_size=2)
  
      loss = agent.actor_loss(time_steps)
      self.evaluate(tf.compat.v1.global_variables_initializer())
      # Ensures that the actor loss calculation does not err out when the critic
      # output has rank > 1.
      self.evaluate(loss)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  from tf_agents.agents.td3 import td3_agent
  from tf_agents.networks import network
  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 7 for calling the TD3AgentTest obj's underlying member method code with example usage: obj.testBatchedActorLoss() and returns: q_value,  network_state