class_docstr: StatsForecast Back Adapter.
  
  StatsForecastBackAdapter is a wrapper for sktime forecasters to be used in
  StatsForecast composite models.
  
  Parameters
  ----------
  estimator : sktime forecaster
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.statsforecast import StatsForecastMSTL
  >>> from sktime.forecasting.ets import AutoETS
  
  >>> y = load_airline()
  >>> trend_forecaster = AutoETS() # doctest: +SKIP
  >>> model = StatsForecastMSTL( # doctest: +SKIP
          season_length=[3,12],
          trend_forecaster=trend_forecaster
      )
  >>> fitted_model = model.fit(y=y) # doctest: +SKIP
  >>> y_pred = fitted_model.predict(fh=[1,2,3]) # doctest: +SKIP
class_docstr_tok: ["StatsForecast","Back","Adapter",".","StatsForecastBackAdapter","is","a","wrapper","for","sktime","forecasters","to","be","used","in","StatsForecast","composite","models",".","Parameters","--","--","--","--","--","estimator",":","sktime","forecaster","Examples","--","--","--","--",">",">",">","from","sktime.datasets","import","load_airline",">",">",">","from","sktime.forecasting.statsforecast","import","StatsForecastMSTL",">",">",">","from","sktime.forecasting.ets","import","AutoETS",">",">",">","y","=","load_airline","(",")",">",">",">","trend_forecaster","=","AutoETS","(",")","#","doctest",":","+SKIP",">",">",">","model","=","StatsForecastMSTL","(","#","doctest",":","+SKIP","season_length=","[","3,12","]",",","trend_forecaster=trend_forecaster",")",">",">",">","fitted_model","=","model.fit","(","y=y",")","#","doctest",":","+SKIP",">",">",">","y_pred","=","fitted_model.predict","(","fh=","[","1,2,3","]",")","#","doctest",":","+SKIP"]
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`
      """
      from sktime.utils.validation._dependencies import (
          _check_soft_dependencies,
      )
  
      del parameter_set  # to avoid being detected as unused by ``vulture`` etc.
  
      if _check_soft_dependencies("statsmodels", severity="none"):
          from sktime.forecasting.theta import ThetaForecaster
          from sktime.forecasting.var import VAR
  
          params = [
              {
                  "estimator": ThetaForecaster(),
              },
              {
                  "estimator": VAR(),
              },
          ]
      else:
          from sktime.forecasting.naive import NaiveForecaster
  
          params = [
              {
                  "estimator": NaiveForecaster(),
              },
          ]
  
      return params
  
imports: from inspect import signature
  from typing import Dict
  from warnings import warn
  import pandas
  from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 8 for calling the StatsForecastBackAdapter obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params