code: def test_regression_in_range():
      X, y = boston_housing_data()
      lr = LinearRegression()
      sfs_r = SFS(
          lr,
          k_features=(1, 13),
          forward=True,
          floating=False,
          scoring="neg_mean_squared_error",
          cv=10,
          verbose=0,
      )
      sfs_r = sfs_r.fit(X, y)
      assert len(sfs_r.k_feature_idx_) == 9
  
      if Version(sklearn_version) < Version("0.20"):
          assert round(sfs_r.k_score_, 4) == -31.1537, round(
              sfs_r.k_score_, 4
          )
      else:
          assert round(sfs_r.k_score_, 4) == -31.1299, round(
              sfs_r.k_score_, 4
          )
  
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 27 for calling a global function with example usage: test_regression_in_range() without return types