code: def test_knn_cv3():
      iris = load_iris()
      X = iris.data
      y = iris.target
      knn = KNeighborsClassifier(n_neighbors=4)
      efs1 = EFS(
          knn,
          min_features=3,
          max_features=3,
          scoring="accuracy",
          cv=4,
          print_progress=False,
      )
      efs1 = efs1.fit(X, y)
      expect = {
          0: {
              "avg_score": 0.9329658605974395,
              "feature_idx": (0, 1, 2),
              "feature_names": ("0", "1", "2"),
              "cv_scores": np.array([0.974, 0.947, 0.892, 0.919]),
          },
          1: {
              "avg_score": 0.9400782361308677,
              "feature_idx": (0, 1, 3),
              "feature_names": ("0", "1", "3"),
              "cv_scores": np.array([0.921, 0.947, 0.919, 0.973]),
          },
          2: {
              "avg_score": 0.9532361308677098,
              "feature_idx": (0, 2, 3),
              "feature_names": ("0", "2", "3"),
              "cv_scores": np.array([0.974, 0.947, 0.919, 0.973]),
          },
          3: {
              "avg_score": 0.97275641025641035,
              "feature_idx": (1, 2, 3),
              "feature_names": ("1", "2", "3"),
              "cv_scores": np.array([0.974, 1.0, 0.946, 0.973]),
          },
      }
  
      if Version(sklearn_version) < Version("1.0"):
          expect[0]["avg_score"] = 0.9391025641025641
          expect[0]["cv_scores"] = np.array(
              [0.974, 0.947, 0.892, 0.946]
          )
          expect[2]["avg_score"] = 0.9529914529914529
  
      if Version(sklearn_version) < Version("0.22"):
          expect[0]["cv_scores"] = np.array(
              [0.97435897, 0.94871795, 0.88888889, 0.94444444]
          )
          expect[1]["cv_scores"] = np.array(
              [0.92307692, 0.94871795, 0.91666667, 0.97222222]
          )
          expect[2]["cv_scores"] = np.array(
              [0.97435897, 0.94871795, 0.91666667, 0.97222222]
          )
          expect[3]["cv_scores"] = np.array(
              [0.97435897, 0.94871795, 0.91666667, 0.97222222]
          )
          expect[1]["avg_score"] = 0.94017094017094016
          assert round(efs1.best_score_, 4) == 0.9728
      else:
          assert round(efs1.best_score_, 4) == 0.9732
  
      dict_compare_utility(d1=expect, d2=efs1.subsets_)
      assert efs1.best_idx_ == (1, 2, 3)
      assert efs1.best_feature_names_ == ("1", "2", "3")
  
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 6 for calling a global function with example usage: test_knn_cv3() without return types