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","."]
docstr: Override function from base class
docstr_tok: ["Override","function","from","base","class"]
code: def set_init_values(self):
      """Override function from base class"""
      init_values = dict()
      if self._seasonality > 1:
          init_sea = np.clip(
              np.random.normal(
                  loc=0, scale=0.05, size=self._seasonality - 1
              ),
              -1.0,
              1.0,
          )
          init_values[
              LatentSamplingParameters.INITIAL_SEASONALITY.value
          ] = init_sea
          self._init_values = init_values
  
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 5 for calling the ETSModel obj's underlying member method code with example usage: obj.set_init_values() without return types