code: def testTrainWithRnn(self):
      action_spec = tensor_spec.BoundedTensorSpec((), tf.int32, 0, 1)
  
      batch_size = 5
      observations = tf.constant(
          [[[1, 2], [3, 4], [5, 6]]] * batch_size, dtype=tf.float32
      )
      actions = tf.constant([[0, 1, 1]] * batch_size, dtype=tf.int32)
      time_steps = ts.TimeStep(
          step_type=tf.constant([[1] * 3] * batch_size, dtype=tf.int32),
          reward=tf.constant([[1] * 3] * batch_size, dtype=tf.float32),
          discount=tf.constant(
              [[1] * 3] * batch_size, dtype=tf.float32
          ),
          observation=[observations],
      )
  
      experience = trajectory.Trajectory(
          step_type=time_steps.step_type,
          observation=observations,
          action=actions,
          policy_info=(),
          next_step_type=time_steps.step_type,
          reward=time_steps.reward,
          discount=time_steps.discount,
      )
  
      categorical_q_rnn_network = DummyCategoricalQRnnNetwork(
          self._obs_spec,
          action_spec,
          conv_layer_params=None,
          input_fc_layer_params=(16,),
          preprocessing_combiner=None,
          lstm_size=(40,),
          output_fc_layer_params=(16,),
      )
  
      counter = common.create_variable("test_train_counter")
  
      agent = categorical_dqn_agent.CategoricalDqnAgent(
          self._time_step_spec,
          action_spec,
          categorical_q_rnn_network,
          optimizer=tf.compat.v1.train.AdamOptimizer(0.001),
      )
  
      # Force variable creation.
      agent.policy.variables()
      if tf.executing_eagerly():
          loss = lambda: agent.train(experience)
      else:
          loss = agent.train(experience)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      self.assertEqual(self.evaluate(counter), 0)
      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.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 14 for calling the CategoricalDqnAgentTest obj's underlying member method code with example usage: obj.testTrainWithRnn() without return types