code: def test_minmaxfeatures_1():
      iris = load_iris()
      X = iris.data
      y = iris.target
      knn = KNeighborsClassifier()
  
      efs = EFS(estimator=knn, min_features=3, max_features=2)
      expect = "min_features must be <= max_features"
      assert_raises(AttributeError, expect, efs.fit, X, y)
  
imports: import numpy
  import pandas
  from numpy.testing import assert_almost_equal
  from packaging.version import Version
  from sklearn import __version__
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import GroupKFold
  from sklearn.neighbors import KNeighborsClassifier
  from mlxtend.classifier import SoftmaxRegression
  from mlxtend.data import boston_housing_data
  from mlxtend.feature_selection import ExhaustiveFeatureSelector
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 4 for calling a global function with example usage: test_minmaxfeatures_1() without return types