code: def testTrainWithRnn(self):
      actor_net = (
          actor_distribution_rnn_network.ActorDistributionRnnNetwork(
              self._obs_spec,
              self._action_spec,
              input_fc_layer_params=None,
              output_fc_layer_params=None,
              conv_layer_params=None,
              lstm_size=(40,),
          )
      )
  
      counter = common.create_variable("test_train_counter")
      agent = reinforce_agent.ReinforceAgent(
          self._time_step_spec,
          self._action_spec,
          actor_network=actor_net,
          optimizer=tf.compat.v1.train.AdamOptimizer(0.001),
          train_step_counter=counter,
      )
  
      batch_size = 5
      observations = tf.constant(
          [[[1, 2], [3, 4], [5, 6]]] * batch_size, dtype=tf.float32
      )
      time_steps = ts.TimeStep(
          step_type=tf.constant(
              [[1, 1, 2]] * 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,
      )
      actions = tf.constant(
          [[[0], [1], [1]]] * batch_size, dtype=tf.float32
      )
  
      experience = trajectory.Trajectory(
          time_steps.step_type,
          observations,
          actions,
          (),
          time_steps.step_type,
          time_steps.reward,
          time_steps.discount,
      )
  
      # 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)
      self.assertEqual(self.evaluate(counter), 1)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  from absl.testing.absltest import mock
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents.reinforce import reinforce_agent
  from tf_agents.networks import actor_distribution_rnn_network
  from tf_agents.networks import network
  from tf_agents.networks import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 21 for calling the ReinforceAgentTest obj's underlying member method code with example usage: obj.testTrainWithRnn() without return types