code: def learn_from_batch(self, batch):
      if (
          not self.networks["main"]
          .online_network.output_heads[0]
          .DND.has_enough_entries(self.ap.algorithm.number_of_knn)
      ):
          return 0, [], 0
      else:
          if not self.training_started:
              self.training_started = True
              screen.log_title(
                  "Finished collecting initial entries in DND. Starting to train network..."
              )
  
      network_keys = self.ap.network_wrappers[
          "main"
      ].input_embedders_parameters.keys()
  
      TD_targets = self.networks["main"].online_network.predict(
          batch.states(network_keys)
      )
      bootstrapped_return_from_old_policy = (
          batch.n_step_discounted_rewards()
      )
      #  only update the action that we have actually done in this transition
      for i in range(batch.size):
          TD_targets[
              i, batch.actions()[i]
          ] = bootstrapped_return_from_old_policy[i]
  
      # set the gradients to fetch for the DND update
      fetches = []
      head = self.networks["main"].online_network.output_heads[0]
      if self.ap.algorithm.propagate_updates_to_DND:
          fetches = [
              head.dnd_embeddings_grad,
              head.dnd_values_grad,
              head.dnd_indices,
          ]
  
      # train the neural network
      result = self.networks["main"].train_and_sync_networks(
          batch.states(network_keys), TD_targets, fetches
      )
  
      total_loss, losses, unclipped_grads = result[:3]
  
      # update the DND keys and values using the extracted gradients
      if self.ap.algorithm.propagate_updates_to_DND:
          embedding_gradients = np.swapaxes(result[-1][0], 0, 1)
          value_gradients = np.swapaxes(result[-1][1], 0, 1)
          indices = np.swapaxes(result[-1][2], 0, 1)
          head.DND.update_keys_and_values(
              batch.actions(),
              embedding_gradients,
              value_gradients,
              indices,
          )
  
      return total_loss, losses, unclipped_grads
  
imports: import os
  import pickle
  from typing import Union, List
  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 DNDQHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
  from rl_coach.core_types import RunPhase, EnvironmentSteps, Episode, StateType
  from rl_coach.exploration_policies.e_greedy import EGreedyParameters
  from rl_coach.logger import screen
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters, MemoryGranularity
  from rl_coach.schedules import ConstantSchedule
label_desc: Use image node_id 3 for calling the NECAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads, int,  list,  int