code: def _forward(self, state: torch.Tensor, tgt_out_idx: torch.Tensor):
      if (state == torch.tensor([1.0, 0.0, 0.0])).all():
          if (tgt_out_idx == torch.tensor([2, 3])).all():
              return 1.0
          else:
              return 2.0
      elif (state == torch.tensor([0.0, 1.0, 0.0])).all():
          if (tgt_out_idx == torch.tensor([2, 3])).all():
              return 3.0
          else:
              return 4.0
      elif (state == torch.tensor([0.0, 0.0, 1.0])).all():
          if (tgt_out_idx == torch.tensor([2, 3])).all():
              return 5.0
          else:
              return 6.0
  
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 3 for calling the FakeSeq2SlateRewardNetwork obj's underlying member method code with example usage: obj._forward(state, tgt_out_idx) and returns: int, int, int, int, int, int