code: def test_randomholdoutsplit_in_grid():
      params = {"n_neighbors": [1, 2, 3, 4, 5]}
  
      if Version(sklearn_version) < Version("0.24.1"):
          grid = GridSearchCV(
              KNeighborsClassifier(),
              iid=False,
              param_grid=params,
              cv=RandomHoldoutSplit(valid_size=0.3, random_seed=123),
          )
      else:
          grid = GridSearchCV(
              KNeighborsClassifier(),
              param_grid=params,
              cv=RandomHoldoutSplit(valid_size=0.3, random_seed=123),
          )
  
      grid.fit(X, y)
      assert grid.n_splits_ == 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 3 for calling a global function with example usage: test_randomholdoutsplit_in_grid() without return types