code: def build_serving_module(
      self,
      trainer_module: ReAgentLightningModule,
      normalization_data_map: Dict[str, NormalizationData],
  ) -> torch.nn.Module:
      assert isinstance(trainer_module, ParametricDQNTrainer)
      net_builder = self.net_builder.value
      return net_builder.build_serving_module(
          trainer_module.q_network,
          normalization_data_map[NormalizationKey.STATE],
          normalization_data_map[NormalizationKey.ACTION],
      )
  
file_path: ReAgent/reagent/model_managers/parametric/parametric_dqn.py
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.parametric_dqn_base import ParametricDQNBase
  from reagent.net_builder.parametric_dqn.fully_connected import FullyConnected
  from reagent.net_builder.unions import ParametricDQNNetBuilder__Union
  from reagent.training import ParametricDQNTrainer, ParametricDQNTrainerParameters, ReAgentLightningModule
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 3 for calling the ParametricDQN obj's underlying member method code with example usage: obj.build_serving_module(trainer_module, normalization_data_map) and returns: net_builder