code: def build_serving_module(
      self,
      trainer_module: ReAgentLightningModule,
      normalization_data_map: Dict[str, NormalizationData],
  ) -> torch.nn.Module:
      assert isinstance(trainer_module, SlateQTrainer)
      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.ITEM],
      )
  
file_path: ReAgent/reagent/model_managers/ranking/slate_q.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.model_managers.slate_q_base import SlateQBase
  from reagent.net_builder.parametric_dqn.fully_connected import FullyConnected
  from reagent.net_builder.unions import ParametricDQNNetBuilder__Union
  from reagent.training import ReAgentLightningModule, SlateQTrainer, SlateQTrainerParameters
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 3 for calling the SlateQ obj's underlying member method code with example usage: obj.build_serving_module(trainer_module, normalization_data_map) and returns: net_builder