code: def test_knn_wo_cv_feature_groups_default():
      iris = load_iris()
      X = iris.data
      y = iris.target
      knn = KNeighborsClassifier(n_neighbors=4)
      sfs1 = SFS(
          knn,
          k_features=3,
          forward=True,
          floating=False,
          cv=0,
          verbose=0,
          feature_groups=[[0], [1], [2], [3]],
      )
      sfs1 = sfs1.fit(X, y)
      expect = {
          1: {
              "avg_score": 0.95999999999999996,
              "cv_scores": np.array([0.96]),
              "feature_idx": (3,),
          },
          2: {
              "avg_score": 0.97333333333333338,
              "cv_scores": np.array([0.97333333]),
              "feature_idx": (2, 3),
          },
          3: {
              "avg_score": 0.97333333333333338,
              "cv_scores": np.array([0.97333333]),
              "feature_idx": (1, 2, 3),
          },
      }
      dict_compare_utility(
          d_actual=sfs1.subsets_, d_desired=expect, decimal=2
      )
  
imports: import numpy
  from numpy.testing import assert_almost_equal
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import roc_auc_score
  from sklearn.neighbors import KNeighborsClassifier
  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 4 for calling a global function with example usage: test_knn_wo_cv_feature_groups_default() without return types