class_docstr: Parameters
  ----------
  seasonality : int
      Length of seasonality
  seasonality_sm_input : float
      float value between [0, 1], applicable only if `seasonality` > 1. A larger value puts
      more weight on the current seasonality.
      If None, the model will estimate this value.
  level_sm_input : float
      float value between [0.0001, 1]. A larger value puts more weight on the current level.
      If None, the model will estimate this value.
class_docstr_tok: ["Parameters","--","--","--","--","--","seasonality",":","int","Length","of","seasonality","seasonality_sm_input",":","float","float","value","between","[","0",",","1","]",",","applicable","only","if","`","seasonality","`",">","1",".","A","larger","value","puts","more","weight","on","the","current","seasonality",".","If","None",",","the","model","will","estimate","this","value",".","level_sm_input",":","float","float","value","between","[","0.0001",",","1","]",".","A","larger","value","puts","more","weight","on","the","current","level",".","If","None",",","the","model","will","estimate","this","value","."]
code: def __init__(
      self,
      seasonality=None,
      seasonality_sm_input=None,
      level_sm_input=None,
      **kwargs,
  ):
      # estimator is created in base class
      super().__init__(**kwargs)
      self.seasonality = seasonality
      self.seasonality_sd = None
  
      # fixed smoothing parameters config
      self.seasonality_sm_input = seasonality_sm_input
      self.level_sm_input = level_sm_input
  
      # set private var to arg value
      # if None set default in _set_default_args()
      self._seasonality = self.seasonality
      self._seasonality_sm_input = self.seasonality_sm_input
      self._level_sm_input = self.level_sm_input
      # handle missing values
      self.is_valid_response = None
  
      self._set_static_attributes()
      self._set_model_param_names()
  
imports: import numpy
  from copy import deepcopy
  import torch
  from enum import Enum
  from ..constants.constants import PredictionKeys, TrainingMetaKeys, PredictionMetaKeys
  from ..exceptions import IllegalArgument, DataInputException
  from .model_template import ModelTemplate
  from ..estimators.stan_estimator import StanEstimatorMCMC, StanEstimatorMAP
  from ..utils.features import moving_average
label_desc: Use image node_id 1 to create a new ETSModel object from inherited base classes: ModelTemplate with example: obj = ETSModel(seasonality, seasonality_sm_input, level_sm_input)