code: def forward(
      self,
      state_and_action_with_presence: Tuple[torch.Tensor, torch.Tensor],
  ) -> torch.Tensor:
      assert self.seq_len == state_and_action_with_presence[0].shape[0]
      state_with_presence, action_with_presence = split_features(
          state_and_action_with_presence,
          self.state_feat_num,
          self.action_feat_num,
      )
      # shape: seq_len, 1, state_feat_dim
      preprocessed_state = self.state_preprocessor(
          state_with_presence[0], state_with_presence[1]
      ).unsqueeze(1)
      # shape: seq_len, 1, action_feat_dim
      preprocessed_action = self.action_preprocessor(
          action_with_presence[0], action_with_presence[1]
      ).unsqueeze(1)
      # shape: (seq_len, )
      reward = self.net(
          preprocessed_state, preprocessed_action
      ).flatten()
      return reward
  
file_path: ReAgent/reagent/prediction/synthetic_reward/synthetic_reward_predictor_wrapper.py
imports: from typing import Tuple
  import torch
  import torch.nn
  from reagent.models.base import ModelBase
  from reagent.preprocessing.preprocessor import Preprocessor
label_desc: Use image node_id 2 for calling the SyntheticRewardPredictorWrapper obj's underlying member method code with example usage: obj.forward(state_and_action_with_presence) and returns: reward