code: def validate_seq2slate_output(
      self, expected_output, wrapper_output
  ) -> None:
      ranked_per_seq_probs, ranked_tgt_out_idx = (
          expected_output.ranked_per_seq_probs,
          expected_output.ranked_tgt_out_idx,
      )
      # -2 to offset padding symbol and decoder start symbol
      ranked_tgt_out_idx -= 2
  
      self.assertTrue(ranked_per_seq_probs == wrapper_output[0])
      self.assertTrue(
          torch.all(torch.eq(ranked_tgt_out_idx, wrapper_output[1]))
      )
  
imports: import random
  import unittest
  import numpy.testing
  import reagent.core.types
  import reagent.models
  import torch
  from reagent.model_utils.seq2slate_utils import Seq2SlateMode, Seq2SlateOutputArch
  from reagent.models.seq2slate import Seq2SlateTransformerNet
  from reagent.prediction.cfeval.predictor_wrapper import BanditRewardNetPredictorWrapper
  from reagent.prediction.predictor_wrapper import ActorPredictorWrapper, ActorWithPreprocessor, DiscreteDqnPredictorWrapper, DiscreteDqnWithPreprocessor, FAKE_STATE_FEATURE_ID, ParametricDqnPredictorWrapper, ParametricDqnWithPreprocessor, Seq2SlatePredictorWrapper, Seq2SlateWithPreprocessor
  from reagent.prediction.ranking.predictor_wrapper import DeterminantalPointProcessPredictorWrapper, Kernel
  from reagent.preprocessing.postprocessor import Postprocessor
  from reagent.preprocessing.preprocessor import Preprocessor
  from reagent.test.prediction.test_prediction_utils import _cont_action_norm, _cont_norm, change_cand_size_slate_ranking
label_desc: Use image node_id 5 for calling the TestPredictorWrapper obj's underlying member method code with example usage: obj.validate_seq2slate_output(expected_output, wrapper_output) without return types