code: def test_select_less_diverse():
      """
      Test case: 10 base classifiers; select 5 based on accuracy,
      then the 3 less diverse
      accuracies (/10): 4 6 1 2 9 8 7 9 3 2
          (should select indices_ 1, 4, 5, 6, 7)
      diversity:        0 8 0 0 1 6 7 2 0 0
          (should select indices_ 4, 5, 7 as most diverse)
  
      """
      pool_classifiers = [create_base_classifier(1) for _ in range(10)]
  
      accuracies = np.array([[4, 6, 1, 2, 9, 8, 7, 9, 3, 2]]) / 10.0
      diversity = np.array([[0, 8, 0, 0, 1, 6, 7, 2, 0, 0]])
      target = DESKNN(
          pool_classifiers,
          k=7,
          pct_accuracy=5.0 / 10,
          pct_diversity=3.0 / 10,
          more_diverse=False,
      )
      target.N_ = 5
      target.J_ = 3
  
      selected_classifiers = target.select(accuracies, diversity)
      expected = np.array([[4, 5, 7]])
  
      assert np.array_equal(
          np.unique(selected_classifiers), np.unique(expected)
      )
  
imports: from unittest.mock import MagicMock
  import numpy
  import pytest
  from sklearn.linear_model import Perceptron
  from sklearn.utils.estimator_checks import check_estimator
  from deslib.des.des_knn import DESKNN
label_desc: Use image node_id 10 for calling a global function with example usage: test_select_less_diverse() without return types