code: def train_network(self, batch, epochs):
      batch_results = []
      for j in range(epochs):
          batch.shuffle()
          batch_results = {
              "total_loss": [],
              "losses": [],
              "unclipped_grads": [],
              "kl_divergence": [],
              "entropy": [],
          }
  
          fetches = [
              self.networks["main"]
              .online_network.output_heads[1]
              .kl_divergence,
              self.networks["main"]
              .online_network.output_heads[1]
              .entropy,
              self.networks["main"]
              .online_network.output_heads[1]
              .likelihood_ratio,
              self.networks["main"]
              .online_network.output_heads[1]
              .clipped_likelihood_ratio,
          ]
  
          for i in range(
              math.ceil(
                  batch.size
                  / self.ap.network_wrappers["main"].batch_size
              )
          ):
              start = i * self.ap.network_wrappers["main"].batch_size
              end = (i + 1) * self.ap.network_wrappers[
                  "main"
              ].batch_size
  
              network_keys = self.ap.network_wrappers[
                  "main"
              ].input_embedders_parameters.keys()
              actions = batch.actions()[start:end]
              gae_based_value_targets = batch.info(
                  "gae_based_value_target"
              )[start:end]
              if (
                  not isinstance(
                      self.spaces.action, DiscreteActionSpace
                  )
                  and len(actions.shape) == 1
              ):
                  actions = np.expand_dims(actions, -1)
  
              # get old policy probabilities and distribution
  
              # TODO-perf - the target network ("old_policy") is not changing. this can be calculated once for all epochs.
              # the shuffling being done, should only be performed on the indices.
              result = self.networks["main"].target_network.predict(
                  {
                      k: v[start:end]
                      for k, v in batch.states(network_keys).items()
                  }
              )
              old_policy_distribution = result[1:]
  
              total_returns = batch.n_step_discounted_rewards(
                  expand_dims=True
              )
  
              # calculate gradients and apply on both the local policy network and on the global policy network
              if self.ap.algorithm.estimate_state_value_using_gae:
                  value_targets = np.expand_dims(
                      gae_based_value_targets, -1
                  )
              else:
                  value_targets = total_returns[start:end]
  
              inputs = copy.copy(
                  {
                      k: v[start:end]
                      for k, v in batch.states(network_keys).items()
                  }
              )
              inputs["output_1_0"] = actions
  
              # The old_policy_distribution needs to be represented as a list, because in the event of
              # discrete controls, it has just a mean. otherwise, it has both a mean and standard deviation
              for input_index, input in enumerate(
                  old_policy_distribution
              ):
                  inputs["output_1_{}".format(input_index + 1)] = input
  
              # update the clipping decay schedule value
              inputs[
                  "output_1_{}".format(len(old_policy_distribution) + 1)
              ] = (
                  self.ap.algorithm.clipping_decay_schedule.current_value
              )
  
              (
                  total_loss,
                  losses,
                  unclipped_grads,
                  fetch_result,
              ) = self.networks["main"].train_and_sync_networks(
                  inputs,
                  [value_targets, batch.info("advantage")[start:end]],
                  additional_fetches=fetches,
              )
  
              batch_results["total_loss"].append(total_loss)
              batch_results["losses"].append(losses)
              batch_results["unclipped_grads"].append(unclipped_grads)
              batch_results["kl_divergence"].append(fetch_result[0])
              batch_results["entropy"].append(fetch_result[1])
  
              self.unclipped_grads.add_sample(unclipped_grads)
              self.value_targets.add_sample(value_targets)
              self.likelihood_ratio.add_sample(fetch_result[2])
              self.clipped_likelihood_ratio.add_sample(fetch_result[3])
  
          for key in batch_results.keys():
              batch_results[key] = np.mean(batch_results[key], 0)
  
          self.value_loss.add_sample(batch_results["losses"][0])
          self.policy_loss.add_sample(batch_results["losses"][1])
          self.loss.add_sample(batch_results["total_loss"])
  
          if (
              self.ap.network_wrappers["main"].learning_rate_decay_rate
              != 0
          ):
              curr_learning_rate = self.networks[
                  "main"
              ].online_network.get_variable_value(
                  self.networks[
                      "main"
                  ].online_network.adaptive_learning_rate_scheme
              )
              self.curr_learning_rate.add_sample(curr_learning_rate)
          else:
              curr_learning_rate = self.ap.network_wrappers[
                  "main"
              ].learning_rate
  
          # log training parameters
          screen.log_dict(
              OrderedDict(
                  [
                      ("Surrogate loss", batch_results["losses"][1]),
                      ("KL divergence", batch_results["kl_divergence"]),
                      ("Entropy", batch_results["entropy"]),
                      ("training epoch", j),
                      ("learning_rate", curr_learning_rate),
                  ]
              ),
              prefix="Policy training",
          )
  
      self.total_kl_divergence_during_training_process = batch_results[
          "kl_divergence"
      ]
      self.entropy.add_sample(batch_results["entropy"])
      self.kl_divergence.add_sample(batch_results["kl_divergence"])
      return batch_results["losses"]
  
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 5 for calling the ClippedPPOAgent obj's underlying member method code with example usage: obj.train_network(batch, epochs) and returns: batch_results