code: def test_regression_sffs():
      X, y = boston_housing_data()
      lr = LinearRegression()
      sfs_r = SFS(
          lr,
          k_features=11,
          forward=True,
          floating=True,
          scoring="neg_mean_squared_error",
          cv=10,
          verbose=0,
      )
      sfs_r = sfs_r.fit(X, y)
      assert sfs_r.k_feature_idx_ == (
          0,
          1,
          3,
          4,
          6,
          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 25 for calling a global function with example usage: test_regression_sffs() without return types