code: def test_classify_instance(create_pool_classifiers):
      query = np.ones((1, 2))
      clustering_test = DESClustering(
          create_pool_classifiers * 4, clustering=KMeans(n_clusters=2)
      )
      clustering_test.n_classes_ = 2
      clustering_test.select = MagicMock(
          return_value=[0, 1, 2, 3, 5, 6, 7, 9]
      )
      predictions = []
      for clf in clustering_test.pool_classifiers:
          predictions.append(clf.predict(query)[0])
  
      predicted = clustering_test.classify_with_ds(
          np.atleast_2d(predictions)
      )
      assert predicted == 0
  
imports: from unittest.mock import MagicMock
  import numpy
  import pytest
  from sklearn.cluster import KMeans
  from sklearn.linear_model import Perceptron
  from sklearn.utils.estimator_checks import check_estimator
  from deslib.des.des_clustering import DESClustering
  from deslib.util.diversity import Q_statistic, ratio_errors, negative_double_fault
label_desc: Use image node_id 7 for calling a global function with example usage: test_classify_instance(create_pool_classifiers) without return types