code: def __init__(
      self,
      reward_model_with_preprocessor: DiscreteDqnWithPreprocessor,
      action_names: List[str],
      state_feature_config: rlt.ModelFeatureConfig,
  ) -> None:
      super().__init__()
      self.reward_model_with_preprocessor = torch.jit.trace(
          reward_model_with_preprocessor,
          reward_model_with_preprocessor.input_prototype(),
      )
      self.action_names = torch.jit.Attribute(action_names, List[str])
  
file_path: ReAgent/reagent/prediction/cfeval/predictor_wrapper.py
imports: import logging
  from typing import List, Tuple
  import torch
  from reagent.core import types
  from reagent.prediction.predictor_wrapper import DiscreteDqnWithPreprocessor
label_desc: Use image node_id 1 to create a new BanditRewardNetPredictorWrapper object from inherited base classes: torch.jit with example: obj = BanditRewardNetPredictorWrapper(reward_model_with_preprocessor, action_names, state_feature_config)