code: def testInitializeRestoreAgent(self, agent_class):
      q_net = DummyNet(self._observation_spec, self._action_spec)
      agent = agent_class(
          self._time_step_spec,
          self._action_spec,
          q_network=q_net,
          optimizer=None,
      )
      observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      time_steps = ts.restart(observations, batch_size=2)
      policy = agent.policy
      action_step = policy.action(time_steps)
      self.evaluate(tf.compat.v1.global_variables_initializer())
  
      checkpoint = tf.train.Checkpoint(agent=agent)
  
      latest_checkpoint = tf.train.latest_checkpoint(
          self.get_temp_dir()
      )
      checkpoint_load_status = checkpoint.restore(latest_checkpoint)
  
      if tf.executing_eagerly():
          self.evaluate(checkpoint_load_status.initialize_or_restore())
          self.assertAllEqual(self.evaluate(action_step.action), [0, 0])
      else:
          with self.cached_session() as sess:
              checkpoint_load_status.initialize_or_restore(sess)
              self.assertAllEqual(sess.run(action_step.action), [0, 0])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import tensorflow
  from tf_agents.agents.dqn import dqn_agent
  from tf_agents.networks import network
  from tf_agents.networks import q_network
  from tf_agents.networks import sequential
  from tf_agents.networks import test_utils
  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
  from tf_agents.utils import test_utils
label_desc: Use image node_id 16 for calling the DqnAgentTest obj's underlying member method code with example usage: obj.testInitializeRestoreAgent(agent_class) without return types