code: def __init__(
      self,
      stop_after: float,
      target: str,
      dtype_dict: Dict[str, str],
      horizon: int,
      ts_analysis: Dict,
      model_path: str = "ets.AutoETS",
      auto_size: bool = True,
      sp: int = None,
      use_stl: bool = True,
      error: str = "add",
      trend: Optional[str] = None,
      damped_trend: bool = False,
      seasonal: Optional[str] = None,
      initialization_method: str = "estimated",
      initial_level: Optional[float] = None,
      initial_trend: Optional[float] = None,
      initial_seasonal: Optional[list] = None,
      bounds: Optional[dict] = None,
      start_params: Optional[list] = None,
      maxiter: int = 1000,
      auto: bool = False,
      information_criterion: str = "aic",
      allow_multiplicative_trend: bool = False,
      restrict: bool = True,
      additive_only: bool = False,
      ignore_inf_ic: bool = True,
      n_jobs: Optional[int] = None,
      random_state: Optional[int] = None,
  ):
      """
      Wrapper for SkTime's AutoETS interface.
  
      :param stop_after: time budget in seconds
      :param target: column containing target time series
      :param dtype_dict: data types for each dataset column
      :param horizon: forecast length
      :param ts_analysis: lightwood-produced stats about input time series
      :param auto_size: whether to filter out old data points if training split is bigger than a certain threshold (defined by the dataset sampling frequency). Enabled by default to avoid long training times in big datasets.
      :param use_stl: Whether to use de-trenders and de-seasonalizers fitted in the timeseries analysis phase.
  
      For the rest of the parameters, please refer to SkTime's documentation.
      """  # noqa
  
      hyperparam_search = False
      model_kwargs = {
          "error": error,
          "trend": trend,
          "damped_trend": damped_trend,
          "seasonal": seasonal,
          "initialization_method": initialization_method,
          "initial_level": initial_level,
          "initial_trend": initial_trend,
          "initial_seasonal": initial_seasonal,
          "bounds": bounds,
          "start_params": start_params,
          "maxiter": maxiter,
          "auto": auto,
          "information_criterion": information_criterion,
          "allow_multiplicative_trend": allow_multiplicative_trend,
          "restrict": restrict,
          "additive_only": additive_only,
          "ignore_inf_ic": ignore_inf_ic,
          "n_jobs": n_jobs,
          "random_state": random_state,
      }
      super().__init__(
          stop_after,
          target,
          dtype_dict,
          horizon,
          ts_analysis,
          model_path,
          model_kwargs,
          auto_size,
          sp,
          hyperparam_search,
          use_stl,
      )
      self.name = "AutoETS"
      self.stable = False
  
imports: from typing import Dict, Optional
  from lightwood.mixer.sktime import SkTime
label_desc: Use image node_id 1 to create a new ETSMixer object from inherited base classes: SkTime with example: obj = ETSMixer(stop_after, target, dtype_dict, horizon, ts_analysis, model_path, auto_size, sp, use_stl, error, trend, damped_trend, seasonal, initialization_method, initial_level, initial_trend, initial_seasonal, bounds, start_params, maxiter, auto, information_criterion, allow_multiplicative_trend, restrict, additive_only, ignore_inf_ic, n_jobs, random_state)