code: def test_ColumnSelector_with_dataframe_in_gridsearch():
      iris = datasets.load_iris()
      X = pd.DataFrame(iris.data, columns=iris.feature_names)
      y = iris.target
      pipe = make_pipeline(ColumnSelector(), LogisticRegression())
      grid = {
          "columnselector__cols": [
              ["petal length (cm)", "petal width (cm)"],
              [
                  "sepal length (cm)",
                  "sepal width (cm)",
                  "petal width (cm)",
              ],
          ],
      }
  
      gsearch1 = GridSearchCV(
          estimator=pipe,
          param_grid=grid,
          cv=5,
          n_jobs=1,
          scoring="accuracy",
          refit=False,
      )
  
      gsearch1.fit(X, y)
      assert gsearch1.best_params_["columnselector__cols"] == [
          "petal length (cm)",
          "petal width (cm)",
      ]
  
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 7 for calling a global function with example usage: test_ColumnSelector_with_dataframe_in_gridsearch() without return types