code: def create_policy(
      self,
      trainer_module: ReAgentLightningModule,
      serving: bool = False,
      normalization_data_map: Optional[
          Dict[str, NormalizationData]
      ] = None,
  ):
      assert isinstance(trainer_module, PPOTrainer)
      if serving:
          assert normalization_data_map is not None
          return create_predictor_policy_from_model(
              self.build_serving_module(
                  trainer_module, normalization_data_map
              )
          )
      else:
          return self._create_policy(trainer_module.scorer)
  
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 PPOTrainer, PPOTrainerParameters, ReAgentLightningModule
  from reagent.workflow.types import ModelFeatureConfigProvider__Union, RewardOptions
label_desc: Use image node_id 4 for calling the PPO 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, self