class_docstr: StatsForecast Complex Exponential Smoothing model.
  
  Direct interface to ``statsforecast.models.AutoCES``,
  from ``statsforecast`` [1]_ by Nixtla.
  
  Automatically selects the best Complex Exponential Smoothing model using an
  information criterion. Default is Akaike Information Criterion (AICc), while
  particular models are estimated using maximum likelihood. The state-space equations
  can be determined based on their $S$ simple, $P$ partial, $Z$ optimized or $N$
  omitted components. The `model` string parameter defines the kind of CES model:
  $N$ for simple CES (without seasonality), $S$ for simple seasonality (lagged CES),
  $P$ for partial seasonality (without complex part), $F$ for full seasonality
  (lagged CES with real and complex seasonal parts).
  
  If the component is selected as 'Z', it operates as a placeholder to ask the
  AutoCES model to figure out the best parameter.
  
  Parameters
  ----------
  season_length : int
      Number of observations per unit of time. Ex: 24 Hourly data.
  model : str
      Controlling state-space-equations.
  
  References
  ----------
  .. [1] https://nixtla.github.io/statsforecast/models.html#autoces
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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`
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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, "model": "Z"}]
  
      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 StatsForecastAutoCES obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params