code: def test_rfecv_cv_results_size(global_random_seed):
      generator = check_random_state(global_random_seed)
      iris = load_iris()
      X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
      y = list(iris.target)  # regression test: list should be supported
  
      # Non-regression test for varying combinations of step and
      # min_features_to_select.
      for step, min_features_to_select in [[2, 1], [2, 2], [3, 3]]:
          rfecv = RFECV(
              estimator=MockClassifier(),
              step=step,
              min_features_to_select=min_features_to_select,
          )
          rfecv.fit(X, y)
  
          score_len = (
              np.ceil((X.shape[1] - min_features_to_select) / step) + 1
          )
  
          for key in rfecv.cv_results_.keys():
              assert len(rfecv.cv_results_[key]) == score_len
  
          assert len(rfecv.ranking_) == X.shape[1]
          assert rfecv.n_features_ >= min_features_to_select
  
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 9 for calling a global function with example usage: test_rfecv_cv_results_size(global_random_seed) without return types