code: def testPolicy(self):
      cloning_net = get_dummy_net(self._action_spec)
      agent = behavioral_cloning_agent.BehavioralCloningAgent(
          self._time_step_spec,
          self._action_spec,
          cloning_network=cloning_net,
          optimizer=None,
      )
      observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      time_steps = ts.restart(observations, batch_size=2)
      policy = agent.policy
      action_step = policy.action(time_steps)
      # Batch size 2.
      self.assertAllEqual(
          [2] + self._action_spec.shape.as_list(),
          action_step.action.shape,
      )
      self.evaluate(tf.compat.v1.global_variables_initializer())
      actions_ = self.evaluate(action_step.action)
      self.assertTrue(all(actions_ <= self._action_spec.maximum))
      self.assertTrue(all(actions_ >= self._action_spec.minimum))
  
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 11 for calling the BehavioralCloningAgentTest obj's underlying member method code with example usage: obj.testPolicy() without return types