code: def test_max_feature_subset_best():
      X, y = boston_housing_data()
      lr = LinearRegression()
  
      sfs = SFS(
          lr, k_features="best", forward=True, floating=False, cv=10
      )
  
      sfs = sfs.fit(X, y)
      assert sfs.k_feature_idx_ == (1, 3, 5, 7, 8, 9, 10, 11, 12)
  
imports: import numpy
  import pandas
  from numpy import nan
  from numpy.testing import assert_almost_equal
  from packaging.version import Version
  from sklearn import __version__
  from sklearn.datasets import load_iris
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import accuracy_score, make_scorer, roc_auc_score
  from sklearn.model_selection import GridSearchCV, GroupKFold
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.pipeline import Pipeline
  from mlxtend.classifier import SoftmaxRegression
  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 37 for calling a global function with example usage: test_max_feature_subset_best() without return types