code: def test_determinantal_point_process_wrapper_rbf_kernel(self) -> None:
      # The second and third items are identical (similarity=1)
      # So the second and third items have strong repulsion
      # The expected ranked indices should be 2, 0, 1
      quality_scores = torch.tensor(
          [
              [4],
              [5],
              [8],
          ]
      )
  
      feature_vectors = torch.tensor(
          [[1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1]]
      )
  
      wrapper = DeterminantalPointProcessPredictorWrapper(
          alpha=1.0, kernel=Kernel.RBF
      )
      ranked_idx, determinants, L = wrapper(
          quality_scores, feature_vectors
      )
      npt.assert_array_almost_equal(ranked_idx, [2, 0, 1])
      npt.assert_array_almost_equal(
          determinants,
          torch.tensor(
              [
                  [16, 25, 64],
                  [885.41766159, 0, wrapper.MIN_VALUE],
                  [wrapper.MIN_VALUE, 0, wrapper.MIN_VALUE],
              ]
          ),
          decimal=3,
      )
      npt.assert_array_almost_equal(
          L,
          [[16, 7.3576, 11.7721], [7.3576, 25, 40], [11.7721, 40, 64]],
          decimal=3,
      )
  
      # Test shorter rerank positions
      # All three items have different categories, so the final order is 1, 2, 0 if
      # rerank the full slate. If rerank_topk=1, then the expected order is 1, 0, 2
      quality_scores = torch.tensor(
          [
              [4],
              [6],
              [5],
          ]
      )
      feature_vectors = torch.tensor(
          [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]
      )
      wrapper = DeterminantalPointProcessPredictorWrapper(
          alpha=1.0, kernel=Kernel.RBF, rerank_topk=1
      )
      ranked_idx, _, _ = wrapper(quality_scores, feature_vectors)
      npt.assert_array_almost_equal(ranked_idx, [1, 0, 2])
  
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 10 for calling the TestPredictorWrapper obj's underlying member method code with example usage: obj.test_determinantal_point_process_wrapper_rbf_kernel() without return types