code: def test_rfe_features_importance():
      generator = check_random_state(0)
      iris = load_iris()
      # Add some irrelevant features. Random seed is set to make sure that
      # irrelevant features are always irrelevant.
      X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
      y = iris.target
  
      clf = RandomForestClassifier(
          n_estimators=20, random_state=generator, max_depth=2
      )
      rfe = RFE(estimator=clf, n_features_to_select=4, step=0.1)
      rfe.fit(X, y)
      assert len(rfe.ranking_) == X.shape[1]
  
      clf_svc = SVC(kernel="linear")
      rfe_svc = RFE(estimator=clf_svc, n_features_to_select=4, step=0.1)
      rfe_svc.fit(X, y)
  
      # Check if the supports are equal
      assert_array_equal(rfe.get_support(), rfe_svc.get_support())
  
imports: from operator import attrgetter
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.compose import TransformedTargetRegressor
  from sklearn.cross_decomposition import CCA, PLSCanonical, PLSRegression
  from sklearn.datasets import load_iris, make_friedman1
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.feature_selection import RFE, RFECV
  from sklearn.impute import SimpleImputer
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import get_scorer, make_scorer, zero_one_loss
  from sklearn.model_selection import GroupKFold, cross_val_score
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.svm import SVC, SVR, LinearSVR
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import ignore_warnings
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 1 for calling a global function with example usage: test_rfe_features_importance() without return types