code: def test_select_best_index(SearchCV):
      """Check the selection strategy of the halving search."""
      results = {  # this isn't a 'real world' result dict
          "iter": np.array([0, 0, 0, 0, 1, 1, 2, 2, 2]),
          "mean_test_score": np.array([4, 3, 5, 1, 11, 10, 5, 6, 9]),
          "params": np.array(
              ["a", "b", "c", "d", "e", "f", "g", "h", "i"]
          ),
      }
  
      # we expect the index of 'i'
      best_index = SearchCV._select_best_index(None, None, results)
      assert best_index == 8
  
imports: from math import ceil
  import numpy
  import pytest
  from scipy.stats import expon, norm, randint
  from sklearn.datasets import make_classification
  from sklearn.dummy import DummyClassifier
  from sklearn.experimental import enable_halving_search_cv
  from sklearn.model_selection import GroupKFold, GroupShuffleSplit, HalvingGridSearchCV, HalvingRandomSearchCV, KFold, LeaveOneGroupOut, LeavePGroupsOut, ShuffleSplit, StratifiedKFold, StratifiedShuffleSplit
  from sklearn.model_selection._search_successive_halving import _SubsampleMetaSplitter, _top_k
  from sklearn.model_selection.tests.test_search import check_cv_results_array_types, check_cv_results_keys
  from sklearn.svm import SVC, LinearSVC
label_desc: Use image node_id 17 for calling a global function with example usage: test_select_best_index(SearchCV) without return types