code: def testTrainWithRNN(self):
      # Hard code a trajectory shaped (time=6, batch=1, ...).
      traj, time_step_spec, action_spec = create_arbitrary_trajectory()
      cloning_net = q_rnn_network.QRnnNetwork(
          time_step_spec.observation, action_spec, lstm_size=(40,)
      )
      agent = behavioral_cloning_agent.BehavioralCloningAgent(
          time_step_spec,
          action_spec,
          cloning_network=cloning_net,
          optimizer=tf.compat.v1.train.AdamOptimizer(
              learning_rate=0.01
          ),
          num_outer_dims=2,
      )
      # Disable clipping to make sure we can see the difference in behavior
      agent.policy._clip = False
      # Remove policy_info, as BehavioralCloningAgent expects none.
      traj = traj.replace(policy_info=())
      # TODO(b/123883319)
      if tf.executing_eagerly():
          train_and_loss = lambda: agent.train(traj)
      else:
          train_and_loss = agent.train(traj)
      self.evaluate(tf.compat.v1.global_variables_initializer())
  
      initial_loss = self.evaluate(train_and_loss).loss
      for _ in range(TRAIN_ITERATIONS - 1):
          loss = self.evaluate(train_and_loss).loss
  
      # We don't necessarily converge to the same actions as in trajectory after
      # 10 steps of an untuned optimizer, but the loss should go down.
      self.assertGreater(initial_loss, loss)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents import test_util
  from tf_agents.agents.behavioral_cloning import behavioral_cloning_agent
  from tf_agents.keras_layers import inner_reshape
  from tf_agents.networks import expand_dims_layer
  from tf_agents.networks import q_network
  from tf_agents.networks import q_rnn_network
  from tf_agents.networks import sequential
  from tf_agents.specs import tensor_spec
  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 10 for calling the BehavioralCloningAgentTest obj's underlying member method code with example usage: obj.testTrainWithRNN() without return types