code: def unpack_parameters(self, params) -> "_OptConfig":
      self.alpha = params[0]
      self.beta = params[1]
      self.gamma = params[2]
      self.level = params[3]
      self.trend = params[4]
      self.phi = params[5]
      self.seasonal = params[6:]
  
      return self
  
imports: from statsmodels.compat.pandas import deprecate_kwarg
  import contextlib
  from typing import Any
  from collections.abc import Hashable, Sequence
  import warnings
  import numpy
  import pandas
  from scipy.optimize import basinhopping, least_squares, minimize
  from scipy.special import inv_boxcox
  from scipy.stats import boxcox
  from statsmodels.tools.validation import array_like, bool_like, dict_like, float_like, int_like, string_like
  from statsmodels.tsa.base.tsa_model import TimeSeriesModel
  from statsmodels.tsa.exponential_smoothing.ets import _initialization_heuristic, _initialization_simple
  from statsmodels.tsa.holtwinters import _exponential_smoothers, _smoothers
  from statsmodels.tsa.holtwinters._exponential_smoothers import HoltWintersArgs
  from statsmodels.tsa.holtwinters._smoothers import to_restricted, to_unrestricted
  from statsmodels.tsa.holtwinters.results import HoltWintersResults, HoltWintersResultsWrapper
  from statsmodels.tsa.tsatools import freq_to_period
label_desc: Use image node_id 1 for calling the _OptConfig obj's underlying member method code with example usage: obj.unpack_parameters(params) and returns: self