code: def learn_from_batch(self, batch):
      network_keys = self.ap.network_wrappers[
          "main"
      ].input_embedders_parameters.keys()
  
      # get the quantiles of the next states and current states
      next_state_quantiles, current_quantiles = self.networks[
          "main"
      ].parallel_prediction(
          [
              (
                  self.networks["main"].target_network,
                  batch.next_states(network_keys),
              ),
              (
                  self.networks["main"].online_network,
                  batch.states(network_keys),
              ),
          ]
      )
  
      # add Q value samples for logging
      self.q_values.add_sample(self.get_q_values(current_quantiles))
  
      # get the optimal actions to take for the next states
      target_actions = np.argmax(
          self.get_q_values(next_state_quantiles), axis=1
      )
  
      # calculate the Bellman update
      batch_idx = list(range(batch.size))
  
      TD_targets = (
          batch.rewards(True)
          + (1.0 - batch.game_overs(True))
          * self.ap.algorithm.discount
          * next_state_quantiles[batch_idx, target_actions]
      )
  
      # get the locations of the selected actions within the batch for indexing purposes
      actions_locations = [
          [b, a] for b, a in zip(batch_idx, batch.actions())
      ]
  
      # calculate the cumulative quantile probabilities and reorder them to fit the sorted quantiles order
      cumulative_probabilities = np.array(
          range(self.ap.algorithm.atoms + 1)
      ) / float(
          self.ap.algorithm.atoms
      )  # tau_i
      quantile_midpoints = 0.5 * (
          cumulative_probabilities[1:] + cumulative_probabilities[:-1]
      )  # tau^hat_i
      quantile_midpoints = np.tile(quantile_midpoints, (batch.size, 1))
      sorted_quantiles = np.argsort(
          current_quantiles[batch_idx, batch.actions()]
      )
      for idx in range(batch.size):
          quantile_midpoints[idx, :] = quantile_midpoints[
              idx, sorted_quantiles[idx]
          ]
  
      # train
      result = self.networks["main"].train_and_sync_networks(
          {
              **batch.states(network_keys),
              "output_0_0": actions_locations,
              "output_0_1": quantile_midpoints,
          },
          TD_targets,
      )
      total_loss, losses, unclipped_grads = result[:3]
  
      return total_loss, losses, unclipped_grads
  
imports: from copy import copy
  from typing import Union
  import numpy
  from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNNetworkParameters, DQNAlgorithmParameters
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.architectures.head_parameters import QuantileRegressionQHeadParameters
  from rl_coach.core_types import StateType
  from rl_coach.schedules import LinearSchedule
label_desc: Use image node_id 6 for calling the QuantileRegressionDQNAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads