code: def testCreateAgentWithPrebuiltPreprocessingLayers(self, agent_class):
      dense_layer = tf.keras.layers.Dense(2)
      q_net = networks_test_utils.KerasLayersNet(
          self._observation_spec, self._action_spec, dense_layer
      )
      with self.assertRaisesRegexp(
          ValueError, "shares weights with the original network"
      ):
          agent_class(
              self._time_step_spec,
              self._action_spec,
              q_network=q_net,
              optimizer=None,
          )
  
      # Explicitly share weights between q and target networks.
      # This would be an unusual setup so we check that an error is thrown.
      q_target_net = networks_test_utils.KerasLayersNet(
          self._observation_spec, self._action_spec, dense_layer
      )
      with self.assertRaisesRegexp(
          ValueError, "shares weights with the original network"
      ):
          agent_class(
              self._time_step_spec,
              self._action_spec,
              q_network=q_net,
              optimizer=None,
              target_q_network=q_target_net,
          )
  
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 4 for calling the DqnAgentTest obj's underlying member method code with example usage: obj.testCreateAgentWithPrebuiltPreprocessingLayers(agent_class) without return types