class_docstr: MultiplexForecaster for selecting among different models.
  
  MultiplexForecaster facilitates a framework for performing
  model selection process over different model classes.
  It should be used in conjunction with ForecastingGridSearchCV
  to get full utilization. It can be used with univariate and
  multivariate forecasters.
  
  MultiplexForecaster is specified with a (named) list of forecasters
  and a selected_forecaster hyper-parameter, which is one of the forecaster names.
  The MultiplexForecaster then behaves precisely as the forecaster with
  name selected_forecaster, ignoring functionality in the other forecasters.
  
  When used with ForecastingGridSearchCV, MultiplexForecaster
  provides an ability to tune across multiple estimators, i.e., to perform AutoML,
  by tuning the selected_forecaster hyper-parameter. This combination will then
  select one of the passed forecasters via the tuning algorithm.
  
  Parameters
  ----------
  forecasters : list of sktime forecasters, or
      list of tuples (str, estimator) of sktime forecasters
      MultiplexForecaster can switch ("multiplex") between these forecasters.
      These are "blueprint" forecasters, states do not change when `fit` is called.
  selected_forecaster: str or None, optional, Default=None.
      If str, must be one of the forecaster names.
          If no names are provided, must coincide with auto-generated name strings.
          To inspect auto-generated name strings, call get_params.
      If None, behaves as if the first forecaster in the list is selected.
      Selects the forecaster as which MultiplexForecaster behaves.
  
  Attributes
  ----------
  forecaster_ : sktime forecaster
      clone of the selected forecaster used for fitting and forecasting.
  _forecasters : list of (str, forecaster) tuples
      str are identical to those passed, if passed strings are unique
      otherwise unique strings are generated from class name; if not unique,
      the string `_[i]` is appended where `[i]` is count of occurrence up until then
  
  Examples
  --------
  >>> from sktime.forecasting.ets import AutoETS
  >>> from sktime.forecasting.model_selection import ForecastingGridSearchCV
  >>> from sktime.split import ExpandingWindowSplitter
  >>> from sktime.forecasting.compose import MultiplexForecaster
  >>> from sktime.forecasting.naive import NaiveForecaster
  >>> from sktime.forecasting.theta import ThetaForecaster
  >>> from sktime.forecasting.model_evaluation import evaluate
  >>> from sktime.datasets import load_shampoo_sales
  >>> y = load_shampoo_sales()
  >>> forecaster = MultiplexForecaster(forecasters=[
  ...     ("ets", AutoETS()),
  ...     ("theta", ThetaForecaster()),
  ...     ("naive", NaiveForecaster())])  # doctest: +SKIP
  >>> cv = ExpandingWindowSplitter(step_length=12)  # doctest: +SKIP
  >>> gscv = ForecastingGridSearchCV(
  ...     cv=cv,
  ...     param_grid={"selected_forecaster":["ets", "theta", "naive"]},
  ...     forecaster=forecaster)  # doctest: +SKIP
  >>> gscv.fit(y)  # doctest: +SKIP
  ForecastingGridSearchCV(...)
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docstr: Forecasters turned into name/est tuples.
docstr_tok: ["Forecasters","turned","into","name\/est","tuples","."]
code: def _forecasters(self):
      """Forecasters turned into name/est tuples."""
      return self._get_estimator_tuples(
          self.forecasters, clone_ests=False
      )
  
file_path: sktime/sktime/forecasting/compose/_multiplexer.py
imports: from sktime.base import _HeterogenousMetaEstimator
  from sktime.datatypes import ALL_TIME_SERIES_MTYPES
  from sktime.forecasting.base._base import BaseForecaster
  from sktime.forecasting.base._delegate import _DelegatedForecaster
label_desc: Use image node_id 2 for calling the MultiplexForecaster obj's underlying member method code with example usage: obj._forecasters() and returns: self