code: def get_reward_model_loss(self, batch: Batch):
      network_keys = self.ap.network_wrappers[
          "reward_model"
      ].input_embedders_parameters.keys()
      current_rewards_prediction_for_all_actions = self.networks[
          "reward_model"
      ].online_network.predict(batch.states(network_keys))
      current_rewards_prediction_for_all_actions[
          range(batch.size), batch.actions()
      ] = batch.rewards()
  
      return self.networks["reward_model"].train_and_sync_networks(
          batch.states(network_keys),
          current_rewards_prediction_for_all_actions,
      )[0]
  
imports: from collections import OrderedDict
  from typing import Union, List
  import numpy
  from rl_coach.agents.agent import Agent
  from rl_coach.core_types import ActionInfo, StateType, Batch
  from rl_coach.filters.filter import NoInputFilter
  from rl_coach.logger import screen
  from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
  from rl_coach.spaces import DiscreteActionSpace
  from copy import deepcopy, copy
label_desc: Use image node_id 11 for calling the ValueOptimizationAgent obj's underlying member method code with example usage: obj.get_reward_model_loss(batch) and returns: self