code: def _test_seq2slate_wrapper(
      self, model: str, output_arch: Seq2SlateOutputArch
  ) -> None:
      state_normalization_parameters = {
          i: _cont_norm() for i in range(1, 5)
      }
      candidate_normalization_parameters = {
          i: _cont_norm() for i in range(101, 106)
      }
      state_preprocessor = Preprocessor(
          state_normalization_parameters, False
      )
      candidate_preprocessor = Preprocessor(
          candidate_normalization_parameters, False
      )
      candidate_size = 10
      slate_size = 4
  
      seq2slate = None
      if model == "transformer":
          seq2slate = Seq2SlateTransformerNet(
              state_dim=len(state_normalization_parameters),
              candidate_dim=len(candidate_normalization_parameters),
              num_stacked_layers=2,
              num_heads=2,
              dim_model=10,
              dim_feedforward=10,
              max_src_seq_len=candidate_size,
              max_tgt_seq_len=slate_size,
              output_arch=output_arch,
              temperature=0.5,
          )
      else:
          raise NotImplementedError(f"model type {model} is unknown")
  
      seq2slate_with_preprocessor = Seq2SlateWithPreprocessor(
          seq2slate,
          state_preprocessor,
          candidate_preprocessor,
          greedy=True,
      )
      wrapper = Seq2SlatePredictorWrapper(seq2slate_with_preprocessor)
  
      (
          state_input_prototype,
          candidate_input_prototype,
      ) = seq2slate_with_preprocessor.input_prototype()
      wrapper_output = wrapper(
          state_input_prototype, candidate_input_prototype
      )
  
      ranking_input = seq2slate_input_prototype_to_ranking_input(
          state_input_prototype,
          candidate_input_prototype,
          state_preprocessor,
          candidate_preprocessor,
      )
      expected_output = seq2slate(
          ranking_input,
          mode=Seq2SlateMode.RANK_MODE,
          tgt_seq_len=candidate_size,
          greedy=True,
      )
      self.validate_seq2slate_output(expected_output, wrapper_output)
  
      # Test Seq2SlatePredictorWrapper can handle variable lengths of inputs
      random_length = random.randint(
          candidate_size + 1, candidate_size * 2
      )
      (
          state_input_prototype,
          candidate_input_prototype,
      ) = change_cand_size_slate_ranking(
          seq2slate_with_preprocessor.input_prototype(), random_length
      )
      wrapper_output = wrapper(
          state_input_prototype, candidate_input_prototype
      )
  
      ranking_input = seq2slate_input_prototype_to_ranking_input(
          state_input_prototype,
          candidate_input_prototype,
          state_preprocessor,
          candidate_preprocessor,
      )
      expected_output = seq2slate(
          ranking_input,
          mode=Seq2SlateMode.RANK_MODE,
          tgt_seq_len=random_length,
          greedy=True,
      )
      self.validate_seq2slate_output(expected_output, wrapper_output)
  
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 8 for calling the TestPredictorWrapper obj's underlying member method code with example usage: obj._test_seq2slate_wrapper(model, output_arch) without return types