code: def test_parametric_wrapper(self) -> None:
      state_normalization_parameters = {
          i: _cont_norm() for i in range(1, 5)
      }
      action_normalization_parameters = {
          i: _cont_norm() for i in range(5, 9)
      }
      state_preprocessor = Preprocessor(
          state_normalization_parameters, False
      )
      action_preprocessor = Preprocessor(
          action_normalization_parameters, False
      )
      dqn = models.FullyConnectedCritic(
          state_dim=len(state_normalization_parameters),
          action_dim=len(action_normalization_parameters),
          sizes=[16],
          activations=["relu"],
      )
      dqn_with_preprocessor = ParametricDqnWithPreprocessor(
          dqn,
          state_preprocessor=state_preprocessor,
          action_preprocessor=action_preprocessor,
      )
      wrapper = ParametricDqnPredictorWrapper(dqn_with_preprocessor)
  
      input_prototype = dqn_with_preprocessor.input_prototype()
      output_action_names, q_value = wrapper(*input_prototype)
      self.assertEqual(output_action_names, ["Q"])
      self.assertEqual(q_value.shape, (1, 1))
  
      expected_output = dqn(
          rlt.FeatureData(state_preprocessor(*input_prototype[0])),
          rlt.FeatureData(action_preprocessor(*input_prototype[1])),
      )
      self.assertTrue((expected_output == q_value).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 3 for calling the TestPredictorWrapper obj's underlying member method code with example usage: obj.test_parametric_wrapper() without return types