class_docstr: StatsForecast Multiple Seasonal-Trend decomposition using LOESS model.
  
  Direct interface to ``statsforecast.models.MSTL``,
  from ``statsforecast`` [1]_ by Nixtla, with a back-adapter that allows
  to use ``sktime`` forecasters as trend forecasters.
  
  The MSTL (Multiple Seasonal-Trend decomposition using LOESS) decomposes the time
  series in multiple seasonalities using LOESS. Then forecasts the trend using
  a custom non-seasonal model (`trend_forecaster`) and each seasonality using a
  SeasonalNaive model. MSTL requires the input time series data to be univariate.
  
  Parameters
  ----------
  season_length : Union[int, List[int]]
      Number of observations per unit of time. For multiple seasonalities use a
      list.
  trend_forecaster : estimator, optional, default=StatsForecastAutoETS()
      Sktime estimator used to make univariate forecasts. Multivariate estimators are
      not supported.
  stl_kwargs : dict, optional
      Extra arguments to pass to [`statsmodels.tsa.seasonal.STL`]
      (https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL).
      The `period` and `seasonal` arguments are reserved.
  pred_int_kwargs : dict, optional
      Extra arguments to pass to [`statsforecast.utils.ConformalIntervals`].
  
  References
  ----------
  .. [1]
      https://nixtla.github.io/statsforecast/src/core/models.html#mstl
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.statsforecast import StatsForecastMSTL
  
  >>> y = load_airline()
  >>> model = StatsForecastMSTL(season_length=[3,12]) # doctest: +SKIP
  >>> fitted_model = model.fit(y=y) # doctest: +SKIP
  >>> y_pred = fitted_model.predict(fh=[1,2,3]) # doctest: +SKIP
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docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
      There are currently no reserved values for forecasters.
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance,
      i.e., `MyClass(**params)` or `MyClass(**params[i])` creates a valid
      test instance. `create_test_instance` uses the first (or only)
      dictionary in `params`
docstr_tok: ["Return","testing","parameter","settings","for","the","estimator",".","Parameters","--","--","--","--","--","parameter_set",":","str",",","default=","''","default","''","Name","of","the","set","of","test","parameters","to","return",",","for","use","in","tests",".","If","no","special","parameters","are","defined","for","a","value",",","will","return","`","``","default","''","`","set",".","There","are","currently","no","reserved","values","for","forecasters",".","Returns","--","--","--","-","params",":","dict","or","list","of","dict",",","default","=","{","}","Parameters","to","create","testing","instances","of","the","class","Each","dict","are","parameters","to","construct","an","``","interesting","''","test","instance",",","i.e.",",","`","MyClass","(","*","*","params",")","`","or","`","MyClass","(","*","*","params","[","i","]",")","`","creates","a","valid","test","instance",".","`","create_test_instance","`","uses","the","first","(","or","only",")","dictionary","in","`","params","`"]
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
          There are currently no reserved values for forecasters.
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance,
          i.e., `MyClass(**params)` or `MyClass(**params[i])` creates a valid
          test instance. `create_test_instance` uses the first (or only)
          dictionary in `params`
      """
      del parameter_set  # to avoid being detected as unused by ``vulture`` etc.
  
      try:
          _check_soft_dependencies("statsmodels")
          from sktime.forecasting.theta import ThetaForecaster
  
          params = [
              {
                  "season_length": [3, 12],
                  "trend_forecaster": ThetaForecaster(),
              },
              {
                  "season_length": 4,
              },
              {
                  "season_length": 4,
                  "pred_int_kwargs": {
                      "n_windows": 2,
                  },
              },
          ]
      except ModuleNotFoundError:
          from sktime.forecasting.naive import NaiveForecaster
  
          params = [
              {
                  "season_length": [3, 12],
                  "trend_forecaster": NaiveForecaster(),
              },
              {
                  "season_length": 4,
              },
          ]
  
      return params
  
imports: from typing import Dict, List, Optional, Union
  from sktime.forecasting.base import BaseForecaster
  from sktime.forecasting.base.adapters._generalised_statsforecast import StatsForecastBackAdapter, _GeneralisedStatsForecastAdapter
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 4 for calling the StatsForecastMSTL obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params