code: def test_actor_wrapper(self) -> None:
      state_normalization_parameters = {
          i: _cont_norm() for i in range(1, 5)
      }
      action_normalization_parameters = {
          i: _cont_action_norm() for i in range(101, 105)
      }
      state_preprocessor = Preprocessor(
          state_normalization_parameters, False
      )
      postprocessor = Postprocessor(
          action_normalization_parameters, False
      )
  
      # Test with FullyConnectedActor to make behavior deterministic
      actor = models.FullyConnectedActor(
          state_dim=len(state_normalization_parameters),
          action_dim=len(action_normalization_parameters),
          sizes=[16],
          activations=["relu"],
      )
      state_feature_config = rlt.ModelFeatureConfig()
      actor_with_preprocessor = ActorWithPreprocessor(
          actor, state_preprocessor, state_feature_config, postprocessor
      )
      wrapper = ActorPredictorWrapper(
          actor_with_preprocessor, state_feature_config
      )
      input_prototype = actor_with_preprocessor.input_prototype()[0]
      action, _log_prob = wrapper(input_prototype)
      self.assertEqual(
          action.shape, (1, len(action_normalization_parameters))
      )
  
      expected_output = postprocessor(
          actor(
              rlt.FeatureData(state_preprocessor(*input_prototype[0]))
          ).action
      )
      self.assertTrue((expected_output == action).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 4 for calling the TestPredictorWrapper obj's underlying member method code with example usage: obj.test_actor_wrapper() without return types