code: def test_cv_generator_raises():
      iris = load_iris()
      X = iris.data
      y = iris.target
      knn = KNeighborsClassifier(n_neighbors=4)
  
      groups = np.arange(len(y)) // 50
      cv_gen = GroupKFold(n_splits=3).split(X, y, groups)
  
      expect = (
          "Input cv is a generator object, which is not supported. "
          "Instead please input an iterable yielding train, test splits. "
          "This can usually be done by passing a cross-validation "
          "generator to the built-in list function. I.e. "
          "cv=list(<cv-generator>)"
      )
  
      assert_raises(
          TypeError,
          expect,
          SFS,
          knn,
          k_features=2,
          cv=cv_gen,
          verbose=0,
          n_jobs=1,
      )
  
imports: import numpy
  import pandas
  from numpy import nan
  from numpy.testing import assert_almost_equal
  from packaging.version import Version
  from sklearn import __version__
  from sklearn.datasets import load_iris
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import accuracy_score, make_scorer, roc_auc_score
  from sklearn.model_selection import GridSearchCV, GroupKFold
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.pipeline import Pipeline
  from mlxtend.classifier import SoftmaxRegression
  from mlxtend.data import boston_housing_data
  from mlxtend.feature_selection import SequentialFeatureSelector
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 32 for calling a global function with example usage: test_cv_generator_raises() without return types