code: def train(self):
      loss = 0
      if self._should_train():
          for network in self.networks.values():
              network.set_is_training(True)
  
          for training_step in range(
              self.ap.algorithm.num_consecutive_training_steps
          ):
              self.networks["actor"].sync()
              self.networks["critic"].sync()
  
              dataset = self.memory.transitions
  
              self.fill_advantages(dataset)
  
              # take only the requested number of steps
              dataset = dataset[
                  : self.ap.algorithm.num_consecutive_playing_steps.num_steps
              ]
  
              value_loss = self.train_value_network(dataset, 1)
              policy_loss = self.train_policy_network(dataset, 10)
  
              self.value_loss.add_sample(value_loss)
              self.policy_loss.add_sample(policy_loss)
  
          for network in self.networks.values():
              network.set_is_training(False)
  
          self.post_training_commands()
          self.training_iteration += 1
          self.update_log()  # should be done in order to update the data that has been accumulated * while not playing *
          return np.append(value_loss, policy_loss)
  
imports: import copy
  from collections import OrderedDict
  from typing import Union
  import numpy
  from rl_coach.agents.actor_critic_agent import ActorCriticAgent
  from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, DistributedTaskParameters
  from rl_coach.core_types import EnvironmentSteps, Batch
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.logger import screen
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
  from rl_coach.utils import force_list
label_desc: Use image node_id 9 for calling the PPOAgent obj's underlying member method code with example usage: obj.train() and returns: np