code: def test_searchcv_callback():
      # Test whether callback is used in BayesSearchCV and
      # whether is can be used to interrupt the search loop
  
      X, y = load_iris(return_X_y=True)
      opt = BayesSearchCV(
          DecisionTreeClassifier(),
          {
              "max_depth": [3],  # additional test for single dimension
              "min_samples_split": Real(0.1, 0.9),
          },
          n_iter=5,
      )
      total_iterations = [0]
  
      def callback(opt_result):
          # this simply counts iterations
          total_iterations[0] += 1
  
          # break the optimization loop at some point
          if total_iterations[0] > 2:
              return True  # True == stop optimization
  
          return False
  
      opt.fit(X, y, callback=callback)
  
      assert total_iterations[0] == 3
  
      # test whether final model was fit
      opt.score(X, y)
  
imports: import pytest
  from sklearn.datasets import load_iris, make_classification
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.svm import SVC, LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.base import clone
  from sklearn.base import BaseEstimator
  from scipy.stats import rankdata
  import numpy
  from numpy.testing import assert_array_equal
  from skopt.space import Real, Categorical, Integer
  from skopt import BayesSearchCV
label_desc: Use image node_id 10 for calling a global function with example usage: test_searchcv_callback() and returns: False, True