code: def test_multiplex_transformer_alone():
      """Test behavior of MultiplexTransformer.
  
      Because MultiplexTransformer is in many ways a wrapper for an underlying
      transformer - we can confirm that if the selected_transformer is set that the
      MultiplexTransformer delegates all its transformation responsibilities as expected.
      """
      y = load_shampoo_sales()
      # randomly make some of the values nans:
      y.loc[y.sample(frac=0.1).index] = np.nan
      # Note - we select two transformers which are deterministic.
      transformer_tuples = [
          ("two", ExponentTransformer(2)),
          ("three", ExponentTransformer(3)),
      ]
      transformer_names = [name for name, _ in transformer_tuples]
      transformers = [
          transformer for _, transformer in transformer_tuples
      ]
      multiplex_transformer = MultiplexTransformer(
          transformers=transformer_tuples
      )
      # for each of the transformers - check that the wrapped transformer predictions
      # agree with the unwrapped transformer (in combo with forecaster) predictions!
      for ind, name in enumerate(transformer_names):
          # make a copy to ensure we don't reference the same objectL
          test_transformer = clone(transformers[ind])
          y_transform_indiv = test_transformer.fit_transform(X=y)
          multiplex_transformer.selected_transformer = name
          # Note- MultiplexTransformer will make a copy of the transformer before fitting.
          y_transform_multi = multiplex_transformer.fit_transform(X=y)
          assert_array_equal(y_transform_indiv, y_transform_multi)
  
file_path: sktime/sktime/transformations/tests/test_multiplexer.py
imports: import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from sklearn.base import clone
  from sktime.datasets import load_shampoo_sales
  from sktime.forecasting.compose import TransformedTargetForecaster
  from sktime.forecasting.model_evaluation import evaluate
  from sktime.forecasting.model_selection import ForecastingGridSearchCV
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.split import ExpandingWindowSplitter
  from sktime.transformations.compose import MultiplexTransformer
  from sktime.transformations.series.exponent import ExponentTransformer
  from sktime.utils.validation.forecasting import check_scoring
label_desc: Use image node_id 1 for calling a global function with example usage: test_multiplex_transformer_alone() without return types