code: def test_regression_sbfs():
      X, y = boston_housing_data()
      lr = LinearRegression()
      sfs_r = SFS(
          lr,
          k_features=1,  # this is number of groups if `feature_groups` is not None
          forward=False,
          floating=True,
          scoring="neg_mean_squared_error",
          cv=10,
          verbose=0,
          feature_groups=[
              [7, 10, 12],
              [0],
              [1],
              [2],
              [3],
              [4],
              [5],
              [6],
              [8],
              [9],
              [11],
          ],
      )
      sfs_r = sfs_r.fit(X, y)
      assert sfs_r.k_feature_idx_ == (7, 10, 12), sfs_r.k_feature_idx_
  
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 5 for calling a global function with example usage: test_regression_sbfs() without return types