code: def test_ColumnSelector_in_gridsearch():
      iris = datasets.load_iris()
      X, y = iris.data, iris.target
      pipe = make_pipeline(
          ColumnSelector(),
          LogisticRegression(multi_class="ovr", solver="liblinear"),
      )
      grid = {
          "columnselector__cols": [[1, 2], [1, 2, 3], 0, [1]],
          "logisticregression__C": [0.1, 1.0, 10.0],
      }
  
      if Version(sklearn_version) < Version("0.24.1"):
          gsearch1 = GridSearchCV(
              estimator=pipe,
              param_grid=grid,
              iid=False,
              cv=5,
              n_jobs=1,
              refit=False,
          )
      else:
          gsearch1 = GridSearchCV(
              estimator=pipe,
              param_grid=grid,
              cv=5,
              n_jobs=1,
              refit=False,
          )
  
      gsearch1.fit(X, y)
      assert gsearch1.best_params_["columnselector__cols"] == [1, 2, 3]
  
file_path: mlxtend/mlxtend/feature_selection/tests/test_column_selector.py
imports: import numpy
  import pandas
  from packaging.version import Version
  from sklearn import __version__
  from sklearn import datasets
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import make_pipeline
  from mlxtend.feature_selection import ColumnSelector
label_desc: Use image node_id 3 for calling a global function with example usage: test_ColumnSelector_in_gridsearch() without return types