code: def train(self):
      episode = self.current_episode_buffer
  
      # check if we should calculate gradients or skip
      num_steps_passed_since_last_update = (
          episode.length() - self.last_gradient_update_step_idx
      )
      is_t_max_steps_passed = (
          num_steps_passed_since_last_update
          >= self.ap.algorithm.num_steps_between_gradient_updates
      )
      if not (is_t_max_steps_passed or episode.is_complete):
          return 0
  
      total_loss = 0
      if num_steps_passed_since_last_update > 0:
          for network in self.networks.values():
              network.set_is_training(True)
  
          # we need to update the returns of the episode until now
          episode.update_transitions_rewards_and_bootstrap_data()
  
          # get t_max transitions or less if the we got to a terminal state
          # will be used for both actor-critic and vanilla PG.
          # In order to get full episodes, Vanilla PG will set the end_idx to a very big value.
          transitions = episode[self.last_gradient_update_step_idx :]
          batch = Batch(transitions)
  
          # move the pointer for the last update step
          if episode.is_complete:
              self.last_gradient_update_step_idx = 0
          else:
              self.last_gradient_update_step_idx = episode.length()
  
          # update the statistics for the variance reduction techniques
          if self.policy_gradient_rescaler in [
              PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_EPISODE,
              PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_TIMESTEP,
          ]:
              self.update_episode_statistics(episode)
  
          # accumulate the gradients
          total_loss, losses, unclipped_grads = self.learn_from_batch(
              batch
          )
  
          # apply the gradients once in every apply_gradients_every_x_episodes episodes
          if (
              self.current_episode
              % self.ap.algorithm.apply_gradients_every_x_episodes
              == 0
          ):
              for network in self.networks.values():
                  network.apply_gradients_and_sync_networks()
          self.training_iteration += 1
  
          for network in self.networks.values():
              network.set_is_training(False)
  
          # run additional commands after the training is done
          self.post_training_commands()
  
      return total_loss
  
file_path: coach/rl_coach/agents/policy_optimization_agent.py
imports: from collections import OrderedDict
  from enum import Enum
  from typing import Union
  import numpy
  from rl_coach.agents.agent import Agent
  from rl_coach.core_types import Batch, ActionInfo
  from rl_coach.logger import screen
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
  from rl_coach.utils import eps
label_desc: Use image node_id 4 for calling the PolicyOptimizationAgent obj's underlying member method code with example usage: obj.train() and returns: total_loss, int