code: def test_knn_wo_cv():
      iris = load_iris()
      X = iris.data
      y = iris.target
      knn = KNeighborsClassifier(n_neighbors=4)
      efs1 = EFS(
          knn,
          min_features=2,
          max_features=3,
          scoring="accuracy",
          cv=0,
          print_progress=False,
      )
      efs1 = efs1.fit(X, y)
      expect = {
          0: {
              "feature_idx": (0, 1),
              "feature_names": ("0", "1"),
              "avg_score": 0.8333333333333334,
              "cv_scores": np.array([0.8333333333333334]),
          },
          1: {
              "feature_idx": (0, 2),
              "feature_names": ("0", "2"),
              "avg_score": 0.95999999999999996,
              "cv_scores": np.array([0.95999999999999996]),
          },
          2: {
              "feature_idx": (0, 3),
              "feature_names": ("0", "3"),
              "avg_score": 0.96666666666666667,
              "cv_scores": np.array([0.96666667]),
          },
          3: {
              "feature_idx": (1, 2),
              "feature_names": ("1", "2"),
              "avg_score": 0.95999999999999996,
              "cv_scores": np.array([0.96]),
          },
          4: {
              "feature_idx": (1, 3),
              "feature_names": ("1", "3"),
              "avg_score": 0.95999999999999996,
              "cv_scores": np.array([0.96]),
          },
          5: {
              "feature_idx": (2, 3),
              "feature_names": ("2", "3"),
              "avg_score": 0.97333333333333338,
              "cv_scores": np.array([0.97333333]),
          },
          6: {
              "feature_idx": (0, 1, 2),
              "feature_names": ("0", "1", "2"),
              "avg_score": 0.95999999999999996,
              "cv_scores": np.array([0.96]),
          },
          7: {
              "feature_idx": (0, 1, 3),
              "feature_names": ("0", "1", "3"),
              "avg_score": 0.96666666666666667,
              "cv_scores": np.array([0.96666667]),
          },
          8: {
              "feature_idx": (0, 2, 3),
              "feature_names": ("0", "2", "3"),
              "avg_score": 0.96666666666666667,
              "cv_scores": np.array([0.96666667]),
          },
          9: {
              "feature_idx": (1, 2, 3),
              "feature_names": ("1", "2", "3"),
              "avg_score": 0.97333333333333338,
              "cv_scores": np.array([0.97333333]),
          },
      }
      dict_compare_utility(d1=expect, d2=efs1.subsets_)
  
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 5 for calling a global function with example usage: test_knn_wo_cv() without return types