code: def test_importances():
      # Check variable importances.
      X, y = datasets.make_classification(
          n_samples=2000,
          n_features=10,
          n_informative=3,
          n_redundant=0,
          n_repeated=0,
          shuffle=False,
          random_state=1,
      )
  
      for alg in ["SAMME", "SAMME.R"]:
          clf = AdaBoostClassifier(algorithm=alg)
  
          clf.fit(X, y)
          importances = clf.feature_importances_
  
          assert importances.shape[0] == 10
          assert (importances[:3, np.newaxis] >= importances[3:]).all()
  
imports: import re
  import numpy
  import pytest
  from sklearn import datasets
  from sklearn.base import BaseEstimator, clone
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.ensemble import AdaBoostClassifier, AdaBoostRegressor
  from sklearn.ensemble._weight_boosting import _samme_proba
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import GridSearchCV, train_test_split
  from sklearn.svm import SVC, SVR
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.utils import shuffle
  from sklearn.utils._mocking import NoSampleWeightWrapper
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal, assert_array_less
  from sklearn.utils.fixes import COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 10 for calling a global function with example usage: test_importances() without return types