code: def create_predictor_policy_from_model(
      serving_module, **kwargs
  ) -> Policy:
      """
      serving_module is the result of ModelManager.build_serving_module().
      This function creates a Policy for gym environments.
      """
      module_name = serving_module.original_name
      if module_name.endswith("DiscreteDqnPredictorWrapper"):
          rl_parameters = kwargs.get("rl_parameters", None)
          return DiscreteDQNPredictorPolicy(
              serving_module, rl_parameters
          )
      elif module_name.endswith("ActorPredictorWrapper"):
          return ActorPredictorPolicy(
              predictor=ActorPredictorUnwrapper(serving_module)
          )
      elif module_name.endswith("ParametricDqnPredictorWrapper"):
          # TODO: remove this dependency
          max_num_actions = kwargs.get("max_num_actions", None)
          assert (
              max_num_actions is not None
          ), f"max_num_actions not given for Parametric DQN."
          q_network = ParametricDqnPredictorUnwrapper(serving_module)
  
          # TODO: write SlateQ Wrapper
          slate_size = kwargs.get("slate_size", None)
          if slate_size is not None:
              scorer = slate_q_serving_scorer(
                  num_candidates=max_num_actions, q_network=q_network
              )
              sampler = TopKSampler(k=slate_size)
          else:
              sampler = GreedyActionSampler()
              scorer = parametric_dqn_serving_scorer(
                  max_num_actions=max_num_actions, q_network=q_network
              )
          return Policy(scorer=scorer, sampler=sampler)
      else:
          raise NotImplementedError(
              f"Predictor policy for serving module {serving_module} not available."
          )
  
imports: from typing import Optional, Tuple, Union
  import reagent.core.types
  import torch
  from reagent.core.fb_checker import IS_FB_ENVIRONMENT
  from reagent.core.parameters import RLParameters
  from reagent.gym.policies import Policy
  from reagent.gym.policies.samplers.discrete_sampler import GreedyActionSampler, SoftmaxActionSampler
  from reagent.gym.policies.samplers.top_k_sampler import TopKSampler
  from reagent.gym.policies.scorers.discrete_scorer import discrete_dqn_serving_scorer, parametric_dqn_serving_scorer
  from reagent.gym.policies.scorers.slate_q_scorer import slate_q_serving_scorer
  from reagent.models.actor import LOG_PROB_MAX, LOG_PROB_MIN
label_desc: Use image node_id 1 for calling a global function with example usage: create_predictor_policy_from_model(serving_module) and returns: DiscreteDQNPredictorPolicy, ActorPredictorPolicy, Policy