code: def learn_from_batch(self, batch):
      network_keys = self.ap.network_wrappers[
          "main"
      ].input_embedders_parameters.keys()
  
      # for the action we actually took, the error is:
      # TD error = r + discount*max(q_st_plus_1) - q_st
      # # for all other actions, the error is 0
      q_st_plus_1, TD_targets = self.networks[
          "main"
      ].parallel_prediction(
          [
              (
                  self.networks["main"].target_network,
                  batch.next_states(network_keys),
              ),
              (
                  self.networks["main"].online_network,
                  batch.states(network_keys),
              ),
          ]
      )
  
      selected_actions = self.select_actions(
          batch.next_states(network_keys), q_st_plus_1
      )
  
      # add Q value samples for logging
      self.q_values.add_sample(TD_targets)
  
      #  only update the action that we have actually done in this transition
      TD_errors = []
      for i in range(batch.size):
          new_target = (
              batch.rewards()[i]
              + (1.0 - batch.game_overs()[i])
              * self.ap.algorithm.discount
              * q_st_plus_1[i][selected_actions[i]]
          )
          TD_errors.append(
              np.abs(new_target - TD_targets[i, batch.actions()[i]])
          )
          TD_targets[i, batch.actions()[i]] = new_target
  
      # update errors in prioritized replay buffer
      importance_weights = (
          self.update_transition_priorities_and_get_weights(
              TD_errors, batch
          )
      )
  
      result = self.networks["main"].train_and_sync_networks(
          batch.states(network_keys),
          TD_targets,
          importance_weights=importance_weights,
      )
  
      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 QHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, MiddlewareScheme
  from rl_coach.core_types import EnvironmentSteps
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
  from rl_coach.schedules import LinearSchedule
label_desc: Use image node_id 4 for calling the DQNAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads