code: def testCreateAgentWithPrebuiltPreprocessingLayersDiffAtoms(self):
      dense_layer = tf.keras.Sequential(
          [
              tf.keras.layers.Dense(10),
              tf.keras.layers.Flatten(),
              tf.keras.layers.Reshape([2, 5]),
          ]
      )
      q_net = KerasLayersNet(
          self._time_step_spec.observation,
          self._action_spec,
          dense_layer,
      )
      dense_layer_target = tf.keras.Sequential(
          [
              tf.keras.layers.Dense(10),
              tf.keras.layers.Reshape([2, 5]),
          ]
      )
      q_bad_target_net = KerasLayersNet(
          self._time_step_spec.observation,
          self._action_spec,
          dense_layer_target,
          num_atoms=3,
      )
      with self.assertRaisesRegexp(
          ValueError, "have different numbers of atoms"
      ):
          categorical_dqn_agent.CategoricalDqnAgent(
              self._time_step_spec,
              self._action_spec,
              categorical_q_network=q_net,
              optimizer=None,
              target_categorical_q_network=q_bad_target_net,
          )
  
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 6 for calling the CategoricalDqnAgentTest obj's underlying member method code with example usage: obj.testCreateAgentWithPrebuiltPreprocessingLayersDiffAtoms() without return types