code: def build_trainer(
      self,
      normalization_data_map: Dict[str, NormalizationData],
      use_gpu: bool,
      reward_options: Optional[RewardOptions] = None,
  ) -> ReinforceTrainer:
      policy_net_builder = self.policy_net_builder.value
      policy_network = policy_net_builder.build_q_network(
          self.state_feature_config,
          normalization_data_map[NormalizationKey.STATE],
          len(self.action_names),
      )
      value_net = None
      value_net_builder = self.value_net_builder
      if value_net_builder:
          value_net_builder = value_net_builder.value
          value_net = value_net_builder.build_value_network(
              normalization_data_map[NormalizationKey.STATE]
          )
      trainer = ReinforceTrainer(
          policy=self._create_policy(policy_network),
          value_net=value_net,
          **self.trainer_param.asdict(),  # pyre-ignore
      )
      return trainer
  
imports: import logging
  from typing import Dict, Optional
  import torch
  from reagent.core import types
  from reagent.core.dataclasses import dataclass, field
  from reagent.core.parameters import NormalizationData, NormalizationKey, param_hash
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.predictor_policies import create_predictor_policy_from_model
  from reagent.gym.policies.samplers.discrete_sampler import SoftmaxActionSampler
  from reagent.model_managers.model_manager import ModelManager
  from reagent.models.model_feature_config_provider import RawModelFeatureConfigProvider
  from reagent.net_builder.discrete_dqn.dueling import Dueling
  from reagent.net_builder.unions import DiscreteDQNNetBuilder__Union, ValueNetBuilder__Union
  from reagent.training import ReAgentLightningModule, ReinforceTrainer, ReinforceTrainerParameters
  from reagent.workflow.types import ModelFeatureConfigProvider__Union, RewardOptions
label_desc: Use image node_id 3 for calling the Reinforce obj's underlying member method code with example usage: obj.build_trainer(normalization_data_map, use_gpu, reward_options) and returns: trainer