code: def _test_seq2slate_model_with_preprocessor(
      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,
      )
      input_prototype = seq2slate_with_preprocessor.input_prototype()
  
      if seq2slate_with_preprocessor.can_be_traced():
          seq2slate_with_preprocessor_jit = torch.jit.trace(
              seq2slate_with_preprocessor,
              seq2slate_with_preprocessor.input_prototype(),
          )
      else:
          seq2slate_with_preprocessor_jit = torch.jit.script(
              seq2slate_with_preprocessor
          )
      expected_output = seq2slate_with_preprocessor(*input_prototype)
      jit_output = seq2slate_with_preprocessor_jit(*input_prototype)
      self.verify_results(expected_output, jit_output)
  
      # Test if scripted model can handle variable lengths of input
      input_prototype = change_cand_size_slate_ranking(
          input_prototype, 20
      )
      expected_output = seq2slate_with_preprocessor(*input_prototype)
      jit_output = seq2slate_with_preprocessor_jit(*input_prototype)
      self.verify_results(expected_output, jit_output)
  
file_path: ReAgent/reagent/test/prediction/test_model_with_preprocessor.py
imports: import unittest
  import numpy.testing
  import torch
  from reagent.model_utils.seq2slate_utils import Seq2SlateOutputArch
  from reagent.models.seq2slate import Seq2SlateTransformerNet
  from reagent.prediction.predictor_wrapper import Seq2SlateWithPreprocessor
  from reagent.preprocessing.preprocessor import Preprocessor
  from reagent.test.prediction.test_prediction_utils import _cont_norm, change_cand_size_slate_ranking
label_desc: Use image node_id 4 for calling the TestModelWithPreprocessor obj's underlying member method code with example usage: obj._test_seq2slate_model_with_preprocessor(model, output_arch) without return types