code: def learn_from_batch(self, batch):
      # batch contains a list of episodes to learn from
      network_keys = self.ap.network_wrappers[
          "main"
      ].input_embedders_parameters.keys()
  
      # get the values for the current states
  
      result = self.networks["main"].online_network.predict(
          batch.states(network_keys)
      )
      current_state_values = result[0]
  
      self.state_values.add_sample(current_state_values)
  
      # the targets for the state value estimator
      num_transitions = batch.size
      state_value_head_targets = np.zeros((num_transitions, 1))
  
      # estimate the advantage function
      action_advantages = np.zeros((num_transitions, 1))
  
      if (
          self.policy_gradient_rescaler
          == PolicyGradientRescaler.A_VALUE
      ):
          if batch.game_overs()[-1]:
              R = 0
          else:
              R = self.networks["main"].online_network.predict(
                  last_sample(batch.next_states(network_keys))
              )[0]
  
          for i in reversed(range(num_transitions)):
              R = batch.rewards()[i] + self.ap.algorithm.discount * R
              state_value_head_targets[i] = R
              action_advantages[i] = R - current_state_values[i]
  
      elif self.policy_gradient_rescaler == PolicyGradientRescaler.GAE:
          # get bootstraps
          bootstrapped_value = self.networks[
              "main"
          ].online_network.predict(
              last_sample(batch.next_states(network_keys))
          )[
              0
          ]
          values = np.append(current_state_values, bootstrapped_value)
          if batch.game_overs()[-1]:
              values[-1] = 0
  
          # get general discounted returns table
          (
              gae_values,
              state_value_head_targets,
          ) = self.get_general_advantage_estimation_values(
              batch.rewards(), values
          )
          action_advantages = np.vstack(gae_values)
      else:
          screen.warning(
              "WARNING: The requested policy gradient rescaler is not available"
          )
  
      action_advantages = action_advantages.squeeze(axis=-1)
      actions = batch.actions()
      if (
          not isinstance(self.spaces.action, DiscreteActionSpace)
          and len(actions.shape) < 2
      ):
          actions = np.expand_dims(actions, -1)
  
      # train
      result = self.networks[
          "main"
      ].online_network.accumulate_gradients(
          {**batch.states(network_keys), "output_1_0": actions},
          [state_value_head_targets, action_advantages],
      )
  
      # logging
      total_loss, losses, unclipped_grads = result[:3]
      self.action_advantages.add_sample(action_advantages)
      self.unclipped_grads.add_sample(unclipped_grads)
      self.value_loss.add_sample(losses[0])
      self.policy_loss.add_sample(losses[1])
  
      return total_loss, losses, unclipped_grads
  
imports: from typing import Union
  import numpy
  import scipy.signal
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import PolicyHeadParameters, VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.exploration_policies.continuous_entropy import ContinuousEntropyParameters
  from rl_coach.logger import screen
  from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
  from rl_coach.utils import last_sample
label_desc: Use image node_id 5 for calling the ActorCriticAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads