code: def learn_from_batch(self, batch):
      network_keys = self.ap.network_wrappers[
          "main"
      ].input_embedders_parameters.keys()
  
      target_values = self.networks["main"].online_network.predict(
          {**batch.states(network_keys)}
      )
  
      branch_to_update = batch.states(
          [self.ap.algorithm.state_key_with_the_class_index]
      )[self.ap.algorithm.state_key_with_the_class_index]
      for idx, branch in enumerate(branch_to_update):
          target_values[branch][idx] = batch.actions()[idx]
  
      result = self.networks["main"].train_and_sync_networks(
          {**batch.states(network_keys)}, target_values
      )
      total_loss, losses, unclipped_grads = result[:3]
  
      return total_loss, losses, unclipped_grads
  
imports: from typing import Union
  from rl_coach.agents.imitation_agent import ImitationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import RegressionHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AgentParameters, MiddlewareScheme, NetworkParameters, AlgorithmParameters
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.non_episodic.balanced_experience_replay import BalancedExperienceReplayParameters
label_desc: Use image node_id 4 for calling the CILAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads