code: def build_trainer(
      self,
      normalization_data_map: Dict[str, NormalizationData],
      use_gpu: bool,
      reward_options: Optional[RewardOptions] = None,
  ) -> SlateQTrainer:
      net_builder = self.net_builder.value
      q_network = net_builder.build_q_network(
          normalization_data_map[NormalizationKey.STATE],
          normalization_data_map[NormalizationKey.ITEM],
      )
  
      q_network_target = q_network.get_target_network()
      return SlateQTrainer(
          q_network=q_network,
          q_network_target=q_network_target,
          slate_size=self.slate_size,
          # pyre-fixme[16]: `SlateQTrainerParameters` has no attribute `asdict`.
          **self.trainer_param.asdict(),
      )
  
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 2 for calling the SlateQ obj's underlying member method code with example usage: obj.build_trainer(normalization_data_map, use_gpu, reward_options) and returns: SlateQTrainer