code: def testUpdateTarget(self):
      agent = categorical_dqn_agent.CategoricalDqnAgent(
          self._time_step_spec,
          self._action_spec,
          self._categorical_net,
          self._optimizer,
      )
  
      observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      time_steps = ts.restart(observations, batch_size=2)
      actions = tf.constant([0, 1], dtype=tf.int32)
      action_steps = policy_step.PolicyStep(actions)
      experience = test_utils.stacked_trajectory_from_transition(
          time_steps, action_steps, time_steps
      )
  
      loss_info = agent._loss(experience)
      update_targets = agent._update_target()
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      losses = self.evaluate(loss_info).loss
      self.assertGreater(losses, 0.0)
      self.evaluate(update_targets)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  from tf_agents.agents.categorical_dqn import categorical_dqn_agent
  from tf_agents.networks import categorical_q_network
  from tf_agents.networks import network
  from tf_agents.networks import q_rnn_network
  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
label_desc: Use image node_id 12 for calling the CategoricalDqnAgentTest obj's underlying member method code with example usage: obj.testUpdateTarget() without return types