code: def train(self):
      if self._should_train():
          for network in self.networks.values():
              network.set_is_training(True)
  
          dataset = self.memory.transitions
          update_internal_state = (
              self.ap.algorithm.update_pre_network_filters_state_on_train
          )
          dataset = self.pre_network_filter.filter(
              dataset,
              deep_copy=False,
              update_internal_state=update_internal_state,
          )
          batch = Batch(dataset)
  
          for training_step in range(
              self.ap.algorithm.num_consecutive_training_steps
          ):
              self.networks["main"].sync()
              self.fill_advantages(batch)
  
              # take only the requested number of steps
              if isinstance(
                  self.ap.algorithm.num_consecutive_playing_steps,
                  EnvironmentSteps,
              ):
                  dataset = dataset[
                      : self.ap.algorithm.num_consecutive_playing_steps.num_steps
                  ]
              shuffle(dataset)
              batch = Batch(dataset)
  
              self.train_network(
                  batch, self.ap.algorithm.optimization_epochs
              )
  
          for network in self.networks.values():
              network.set_is_training(False)
  
          self.post_training_commands()
          self.training_iteration += 1
          # should be done in order to update the data that has been accumulated * while not playing *
          self.update_log()
          return None
  
imports: import copy
  import math
  from collections import OrderedDict
  from random import shuffle
  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
  from rl_coach.core_types import EnvironmentSteps, Batch, EnvResponse, StateType
  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.schedules import ConstantSchedule
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
label_desc: Use image node_id 7 for calling the ClippedPPOAgent obj's underlying member method code with example usage: obj.train() and returns: None