class_docstr: Class to build Actor Policies.
code: def _apply_actor_network(
      self, observation, step_type, policy_state, mask=None
  ):
      if self._observation_normalizer:
          observation = self._observation_normalizer.normalize(
              observation
          )
      if mask is None:
          return self._actor_network(
              observation,
              step_type=step_type,
              network_state=policy_state,
              training=self._training,
          )
      else:
          return self._actor_network(
              observation,
              step_type=step_type,
              network_state=policy_state,
              training=self._training,
              mask=mask,
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Optional, Text
  import gin
  import tensorflow
  import tensorflow_probability
  from tf_agents.distributions import utils
  from tf_agents.networks import network
  from tf_agents.policies import tf_policy
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import tensor_normalizer
label_desc: Use image node_id 2 for calling the ActorPolicy obj's underlying member method code with example usage: obj._apply_actor_network(observation, step_type, policy_state, mask) and returns: self, self