code: def test_check_feature_groups():
      iris = load_iris()
      X = iris.data
      y = iris.target
      lr = SoftmaxRegression(random_seed=123)
      sfs1 = SFS(
          lr,
          k_features=2,
          forward=True,
          floating=False,
          scoring="accuracy",
          feature_groups=[[2, 3], [0], [1]],
          fixed_features=[0, 2, 3],
          cv=0,
          verbose=0,
          n_jobs=1,
      )
  
      sfs1 = sfs1.fit(X, y)
      assert sfs1.k_feature_idx_ == (0, 2, 3), sfs1.k_feature_idx_
  
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 47 for calling a global function with example usage: test_check_feature_groups() without return types