code: def create_policy(
      self,
      trainer_module: ReAgentLightningModule,
      serving: bool = False,
      normalization_data_map: Optional[
          Dict[str, NormalizationData]
      ] = None,
  ):
      if serving:
          assert normalization_data_map
          return create_predictor_policy_from_model(
              self.build_serving_module(
                  trainer_module, normalization_data_map
              ),
              # pyre-fixme[16]: `SlateQBase` has no attribute `num_candidates`.
              max_num_actions=self.num_candidates,
              # pyre-fixme[16]: `SlateQBase` has no attribute `slate_size`.
              slate_size=self.slate_size,
          )
      else:
          scorer = slate_q_scorer(
              num_candidates=self.num_candidates,
              q_network=trainer_module.q_network,
          )
          sampler = TopKSampler(k=self.slate_size)
          return Policy(scorer=scorer, sampler=sampler)
  
imports: import logging
  from typing import Dict, List, Optional, Tuple
  import reagent.core.types
  from reagent.core.dataclasses import dataclass
  from reagent.core.parameters import NormalizationData
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.predictor_policies import create_predictor_policy_from_model
  from reagent.gym.policies.samplers.top_k_sampler import TopKSampler
  from reagent.gym.policies.scorers.slate_q_scorer import slate_q_scorer
  from reagent.model_managers.model_manager import ModelManager
  from reagent.preprocessing.normalization import get_feature_config
  from reagent.reporting.slate_q_reporter import SlateQReporter
  from reagent.training import ReAgentLightningModule
  from reagent.workflow.types import PreprocessingOptions
label_desc: Use image node_id 2 for calling the SlateQBase obj's underlying member method code with example usage: obj.create_policy(trainer_module, serving, normalization_data_map) and returns: create_predictor_policy_from_model, Policy