code: def testPolicy(self):
      agent = reinforce_agent.ReinforceAgent(
          self._time_step_spec,
          self._action_spec,
          actor_network=DummyActorNet(
              self._obs_spec, self._action_spec, unbounded_actions=False
          ),
          optimizer=None,
      )
      observations = tf.constant([[1, 2]], dtype=tf.float32)
      time_steps = ts.restart(observations, batch_size=2)
      actions = agent.policy.action(time_steps).action
      self.assertEqual(actions.shape.as_list(), [1, 1])
      self.evaluate(tf.compat.v1.global_variables_initializer())
      action_values = self.evaluate(actions)
      tf.nest.map_structure(
          lambda v, s: self.assertAllInRange(v, s.minimum, s.maximum),
          action_values,
          self._action_spec,
      )
  
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 19 for calling the ReinforceAgentTest obj's underlying member method code with example usage: obj.testPolicy() without return types