code: def test_predefinedholdoutsplit_in_sfs():
      h_iter = PredefinedHoldoutSplit(valid_indices=[0, 1, 99])
      knn = KNeighborsClassifier(n_neighbors=4)
  
      sfs1 = SFS(
          knn,
          k_features=3,
          forward=True,
          floating=False,
          verbose=2,
          scoring="accuracy",
          cv=h_iter,
      )
  
      sfs1 = sfs1.fit(X, y)
      d = sfs1.get_metric_dict()
      assert d[1]["cv_scores"].shape[0] == 1
  
file_path: mlxtend/mlxtend/evaluate/tests/test_holdout.py
imports: import numpy
  from packaging.version import Version
  from sklearn import __version__
  from sklearn.model_selection import GridSearchCV
  from sklearn.neighbors import KNeighborsClassifier
  from mlxtend.data import iris_data
  from mlxtend.evaluate import PredefinedHoldoutSplit, RandomHoldoutSplit
  from mlxtend.feature_selection import SequentialFeatureSelector
label_desc: Use image node_id 5 for calling a global function with example usage: test_predefinedholdoutsplit_in_sfs() without return types