code: def learn_from_batch(self, batch):
      network_keys = self.ap.network_wrappers[
          "main"
      ].input_embedders_parameters.keys()
  
      # When using a policy head, the targets refer to the advantages that we are normally feeding the head with.
      # In this case, we need the policy head to just predict probabilities, so while we usually train the network
      # with log(Pi)*Advantages, in this specific case we will train it to log(Pi), which after the softmax will
      # predict Pi (=probabilities)
      targets = np.ones(batch.actions().shape[0])
  
      result = self.networks["main"].train_and_sync_networks(
          {**batch.states(network_keys), "output_0_0": batch.actions()},
          targets,
      )
      total_loss, losses, unclipped_grads = result[:3]
  
      return total_loss, losses, unclipped_grads
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.imitation_agent import ImitationAgent
  from rl_coach.architectures.head_parameters import PolicyHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.base_parameters import AgentParameters, AlgorithmParameters, NetworkParameters, MiddlewareScheme
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
label_desc: Use image node_id 3 for calling the BCAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads