code: def test_discrete_wrapper(self) -> None:
      ids = range(1, 5)
      state_normalization_parameters = {i: _cont_norm() for i in ids}
      state_preprocessor = Preprocessor(
          state_normalization_parameters, False
      )
      action_dim = 2
      dqn = models.FullyConnectedDQN(
          state_dim=len(state_normalization_parameters),
          action_dim=action_dim,
          sizes=[16],
          activations=["relu"],
      )
      state_feature_config = rlt.ModelFeatureConfig(
          float_feature_infos=[
              rlt.FloatFeatureInfo(feature_id=i, name=f"feat_{i}")
              for i in ids
          ]
      )
      dqn_with_preprocessor = DiscreteDqnWithPreprocessor(
          dqn, state_preprocessor, state_feature_config
      )
      action_names = ["L", "R"]
      wrapper = DiscreteDqnPredictorWrapper(
          dqn_with_preprocessor, action_names, state_feature_config
      )
      input_prototype = dqn_with_preprocessor.input_prototype()[0]
      output_action_names, q_values = wrapper(input_prototype)
      self.assertEqual(action_names, output_action_names)
      self.assertEqual(q_values.shape, (1, 2))
  
      state_with_presence = input_prototype.float_features_with_presence
      expected_output = dqn(
          rlt.FeatureData(state_preprocessor(*state_with_presence))
      )
      self.assertTrue((expected_output == q_values).all())
  
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 1 for calling the TestPredictorWrapper obj's underlying member method code with example usage: obj.test_discrete_wrapper() without return types