code: def forward(
      self, state: rlt.ServingFeatureData
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      reward_predictions = self.reward_model_with_preprocessor(state)
      num_examples = reward_predictions.size()[0]
      num_actions = len(self.action_names)
      assert reward_predictions.shape == (
          num_examples,
          num_actions,
      ), f"Invalid shape {reward_predictions.shape} != ({num_examples}, {num_actions})"
      mask = torch.ones_like(reward_predictions, dtype=torch.uint8)
      return (reward_predictions, mask)
  
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 2 for calling the BanditRewardNetPredictorWrapper obj's underlying member method code with example usage: obj.forward(state) and returns: reward_predictions,  mask