code: def learn_from_batch(self, batch):
      network_keys = self.ap.network_wrappers[
          "main"
      ].input_embedders_parameters.keys()
  
      # TD error = r + discount*v_st_plus_1 - q_st
      v_st_plus_1 = self.networks["main"].target_network.predict(
          batch.next_states(network_keys),
          self.networks["main"].target_network.output_heads[0].V,
          squeeze_output=False,
      )
      TD_targets = (
          np.expand_dims(batch.rewards(), -1)
          + (1.0 - np.expand_dims(batch.game_overs(), -1))
          * self.ap.algorithm.discount
          * v_st_plus_1
      )
  
      self.TD_targets.add_sample(TD_targets)
  
      result = self.networks["main"].train_and_sync_networks(
          {
              **batch.states(network_keys),
              "output_0_0": batch.actions(
                  len(batch.actions().shape) == 1
              ),
          },
          TD_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.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import NAFHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters
  from rl_coach.core_types import ActionInfo, EnvironmentSteps
  from rl_coach.exploration_policies.ou_process import OUProcessParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import BoxActionSpace
label_desc: Use image node_id 3 for calling the NAFAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads