class_docstr: Base class for continuous actor net builder.
class_docstr_tok: ["Base","class","for","continuous","actor","net","builder","."]
code: def build_ranking_serving_module(
      self,
      actor: ModelBase,
      state_normalization_data: NormalizationData,
      candidate_normalization_data: NormalizationData,
      num_candidates: int,
      action_normalization_data: NormalizationData,
  ) -> torch.nn.Module:
      state_preprocessor = Preprocessor(
          state_normalization_data.dense_normalization_parameters,
          use_gpu=False,
      )
      candidate_preprocessor = Preprocessor(
          candidate_normalization_data.dense_normalization_parameters,
          use_gpu=False,
      )
      postprocessor = Postprocessor(
          action_normalization_data.dense_normalization_parameters,
          use_gpu=False,
      )
      actor_with_preprocessor = RankingActorWithPreprocessor(
          model=actor.cpu_model().eval(),
          state_preprocessor=state_preprocessor,
          candidate_preprocessor=candidate_preprocessor,
          num_candidates=num_candidates,
          action_postprocessor=postprocessor,
      )
      action_features = Preprocessor(
          action_normalization_data.dense_normalization_parameters,
          use_gpu=False,
      ).sorted_features
      return RankingActorPredictorWrapper(
          actor_with_preprocessor, action_features
      )
  
file_path: ReAgent/reagent/net_builder/continuous_actor_net_builder.py
imports: import abc
  import reagent.core.types
  import torch
  from reagent.core.fb_checker import IS_FB_ENVIRONMENT
  from reagent.core.parameters import NormalizationData
  from reagent.models.base import ModelBase
  from reagent.prediction.predictor_wrapper import ActorWithPreprocessor, RankingActorWithPreprocessor
  from reagent.preprocessing.postprocessor import Postprocessor
  from reagent.preprocessing.preprocessor import Preprocessor
label_desc: Use image node_id 4 for calling the ContinuousActorNetBuilder obj's underlying member method code with example usage: obj.build_ranking_serving_module(actor, state_normalization_data, candidate_normalization_data, num_candidates, action_normalization_data) and returns: RankingActorPredictorWrapper