code: def learn_from_batch(self, batch):
      network_keys = self.ap.network_wrappers[
          "main"
      ].input_embedders_parameters.keys()
  
      # for the 1-step, we use the double-dqn target. hence actions are taken greedily according to the online network
      selected_actions = np.argmax(
          self.networks["main"].online_network.predict(
              batch.next_states(network_keys)
          ),
          1,
      )
  
      # TD_targets are initialized with the current prediction so that we will
      #  only update the action that we have actually done in this transition
      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),
              ),
          ]
      )
  
      total_returns = batch.n_step_discounted_rewards()
  
      for i in range(batch.size):
          one_step_target = (
              batch.rewards()[i]
              + (1.0 - batch.game_overs()[i])
              * self.ap.algorithm.discount
              * q_st_plus_1[i][selected_actions[i]]
          )
          monte_carlo_target = total_returns[i]
          TD_targets[i, batch.actions()[i]] = (
              (1 - self.mixing_rate) * one_step_target
              + self.mixing_rate * monte_carlo_target
          )
  
      result = self.networks["main"].train_and_sync_networks(
          batch.states(network_keys), TD_targets
      )
      total_loss, losses, unclipped_grads = result[:3]
  
      return total_loss, losses, unclipped_grads
  
file_path: coach/rl_coach/agents/mmc_agent.py
imports: from typing import Union
  import numpy
  from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNAlgorithmParameters
  from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
label_desc: Use image node_id 3 for calling the MixedMonteCarloAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads