class_docstr: Statsforecast AutoTheta estimator.
  
  Direct interface to ``statsforecast.models.AutoTheta`` by Nixtla.
  
  This estimator directly interfaces ``AutoTheta``,
  from ``statsforecast`` [1]_ by Nixtla.
  
  AutoTheta model automatically selects the best Theta (Standard Theta Model ("STM"),
  Optimized Theta Model ("OTM"), Dynamic Standard Theta Model ("DSTM"), Dynamic
  Optimized Theta Model ("DOTM")) model using mse.
  
  Parameters
  ----------
  season_length : int, optional, default=1
      number of observations per unit of time (e.g. 24 for hourly data), by default 1
  
  decomposition_type : str, optional, default="multipliciative"
      possible values: "additive", "multiplicative"
      type of seasonal decomposition, by default "multiplicative"
  
  model : Optional[str], optional
      controlling Theta Model, by default searches the best model
  
  References
  ----------
  .. [1] https://nixtla.github.io/statsforecast/models.html#autotheta
  
  See Also
  --------
  ThetaForecaster
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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.
  
      params = [{}, {"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 StatsForecastAutoTheta obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params