code: def test_multiplex_forecaster_alone():
      """Test results of MultiplexForecaster.
  
      Because MultiplexForecaster is in many ways a wrapper for an underlying
      forecaster - we can confirm that if the selected_forecaster is set that the
      MultiplexForecaster performs as expected.
      """
      from numpy.testing import assert_array_equal
  
      y = load_shampoo_sales()
      # Note - we select two forecasters which are deterministic.
      forecaster_tuples = [
          ("naive", NaiveForecaster()),
          ("theta", ThetaForecaster()),
      ]
      forecaster_names = [name for name, _ in forecaster_tuples]
      forecasters = [forecaster for _, forecaster in forecaster_tuples]
      multiplex_forecaster = MultiplexForecaster(
          forecasters=forecaster_tuples
      )
      fh_test = [1, 2, 3]
      # for each of the forecasters - check that the wrapped forecaster predictions
      # agree with the unwrapped forecaster predictions!
      for ind, name in enumerate(forecaster_names):
          # make a copy to ensure we don't reference the same objectL
          test_forecaster = forecasters[ind].clone()
          test_forecaster.fit(y)
          multiplex_forecaster.selected_forecaster = name
          # Note- MultiplexForecaster will make a copy of the forecaster before fitting.
          multiplex_forecaster.fit(y)
          y_pred_indiv = test_forecaster.predict(fh=fh_test)
          y_pred_multi = multiplex_forecaster.predict(fh=fh_test)
          assert_array_equal(y_pred_indiv, y_pred_multi)
  
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 2 for calling a global function with example usage: test_multiplex_forecaster_alone() without return types