code: def test_multiplex_with_grid_search():
      """Test MultiplexForecaster perfromas as expected with ForecastingGridSearchCV.
  
      Because the typical use case of MultiplexForecaster is to use it with the
      ForecastingGridSearchCV forecaster - here we simply test that the best
      "selected_forecaster" for MultiplexForecaster found using ForecastingGridSearchCV
      is the same forecaster we would find if we evaluated all the forecasters in
      MultiplexForecaster independently.
      """
      y = load_shampoo_sales()
      forecasters = [
          ("naive1", NaiveForecaster()),
          ("naive2", NaiveForecaster(strategy="mean")),
      ]
      multiplex_forecaster = MultiplexForecaster(
          forecasters=forecasters
      )
      forecaster_names = [name for name, _ in forecasters]
      cv = ExpandingWindowSplitter(step_length=12)
      gscv = ForecastingGridSearchCV(
          cv=cv,
          param_grid={"selected_forecaster": forecaster_names},
          forecaster=multiplex_forecaster,
      )
      gscv.fit(y)
      gscv_best_name = gscv.best_forecaster_.selected_forecaster
      best_name = _score_forecasters(forecasters, cv, y)
      assert gscv_best_name == best_name
  
file_path: sktime/sktime/forecasting/compose/tests/test_multiplex.py
imports: import pytest
  from sktime.datasets import load_shampoo_sales
  from sktime.forecasting.arima import AutoARIMA
  from sktime.forecasting.compose import MultiplexForecaster
  from sktime.forecasting.ets import AutoETS
  from sktime.forecasting.model_evaluation import evaluate
  from sktime.forecasting.model_selection import ForecastingGridSearchCV
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.forecasting.theta import ThetaForecaster
  from sktime.split import ExpandingWindowSplitter
  from sktime.utils.validation._dependencies import _check_estimator_deps
  from sktime.utils.validation.forecasting import check_scoring
label_desc: Use image node_id 3 for calling a global function with example usage: test_multiplex_with_grid_search() without return types