code: def testPolicyGradientLoss(self):
      agent = reinforce_agent.ReinforceAgent(
          self._time_step_spec,
          self._action_spec,
          actor_network=DummyActorNet(
              self._obs_spec, self._action_spec, unbounded_actions=True
          ),
          optimizer=None,
      )
  
      observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      time_steps = ts.restart(observations, batch_size=2)
      actions = tf.constant([[0], [1]], dtype=tf.float32)
      actions_distribution = agent.collect_policy.distribution(
          time_steps
      ).action
      returns = tf.constant([1.9, 1.0], dtype=tf.float32)
  
      expected_loss = 10.983667373657227
      loss = agent.policy_gradient_loss(
          actions_distribution,
          actions,
          time_steps.is_last(),
          returns,
          1,
      )
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      loss_ = self.evaluate(loss)
      self.assertAllClose(loss_, expected_loss)
  
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 4 for calling the ReinforceAgentTest obj's underlying member method code with example usage: obj.testPolicyGradientLoss() without return types