code: def serving_module_names(self):
      module_names = ["default_model"]
      if len(self.action_names) == 2:
          module_names.append("binary_difference_scorer")
      if self.eval_parameters.calc_cpe_in_training:
          module_names.append("reward_model")
      return module_names
  
imports: import logging
  from typing import Dict, Optional
  import torch
  from reagent.core.dataclasses import dataclass, field
  from reagent.core.parameters import NormalizationData, NormalizationKey, param_hash
  from reagent.evaluation.evaluator import get_metrics_to_score
  from reagent.model_managers.discrete_dqn_base import DiscreteDQNBase
  from reagent.net_builder.discrete_dqn.dueling import Dueling
  from reagent.net_builder.discrete_dqn.fully_connected import FullyConnected
  from reagent.net_builder.unions import DiscreteDQNNetBuilder__Union
  from reagent.prediction.cfeval.predictor_wrapper import BanditRewardNetPredictorWrapper
  from reagent.reporting.discrete_dqn_reporter import DiscreteDQNReporter
  from reagent.training import DQNTrainer, DQNTrainerParameters, ReAgentLightningModule
  from reagent.training.dqn_trainer_base import DQNTrainerBaseLightning
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 6 for calling the DiscreteDQN obj's underlying member method code with example usage: obj.serving_module_names() and returns: module_names