code: def forward(
      self,
      input: rlt.PreprocessedRankingInput,
      mode: str,
      greedy: Optional[bool] = None,
  ):
      # The creation of evaluation data pages only uses these specific arguments
      assert mode in (
          Seq2SlateMode.RANK_MODE,
          Seq2SlateMode.PER_SEQ_LOG_PROB_MODE,
      )
      if mode == Seq2SlateMode.RANK_MODE:
          assert greedy
          return rlt.RankingOutput(
              ranked_tgt_out_idx=torch.tensor(
                  [[2, 3], [3, 2], [2, 3]]
              ).long()
          )
      return rlt.RankingOutput(
          log_probs=torch.log(
              torch.tensor([0.4, 0.3, 0.7]).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 FakeSeq2SlateTransformerNet obj's underlying member method code with example usage: obj.forward(input, mode, greedy) and returns: rlt, rlt