code: def test_max_feature_subset_parsimonious():
      X, y = boston_housing_data()
      lr = LinearRegression()
  
      sfs = SFS(
          lr,
          k_features="parsimonious",
          forward=True,
          floating=False,
          cv=10,
          feature_groups=[
              [0],
              [1, 3],
              [2, 4],
              [5, 10, 11, 12],
              [6],
              [7],
              [8, 9],
          ],
      )
  
      sfs = sfs.fit(X, y)
      assert sfs.k_feature_idx_ == (5, 10, 11, 12)
  
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 9 for calling a global function with example usage: test_max_feature_subset_parsimonious() without return types