code: def forward(
      self,
      state: torch.Tensor,
      src_seq: torch.Tensor,
      tgt_out_seq: torch.Tensor,
      src_src_mask: torch.Tensor,
      tgt_out_idx: torch.Tensor,
  ):
      batch_size = state.shape[0]
      rewards = []
      for i in range(batch_size):
          rewards.append(self._forward(state[i], tgt_out_idx[i]))
      return torch.tensor(rewards).unsqueeze(1)
  
imports: import logging
  import unittest
  from typing import Optional
  import numpy
  import torch
  import torch.nn
  from reagent.core import types
  from reagent.evaluation.doubly_robust_estimator import DoublyRobustEstimator
  from reagent.evaluation.evaluation_data_page import EvaluationDataPage
  from reagent.evaluation.ope_adapter import OPEstimatorAdapter
  from reagent.model_utils.seq2slate_utils import Seq2SlateMode
  from reagent.ope.estimators.contextual_bandits_estimators import SwitchDREstimator, SwitchEstimator
label_desc: Use image node_id 2 for calling the FakeSeq2SlateRewardNetwork obj's underlying member method code with example usage: obj.forward(state, src_seq, tgt_out_seq, src_src_mask, tgt_out_idx) and returns: torch