code: def initial_parameters(
      seasonal_periods,
      k_vector,
      use_trend,
      use_damped_trend,
      ar_coeffs,
      ma_coeffs,
  ):
      alpha = 0.09
  
      if use_trend:
          adj_beta = 1
          beta = 0.05
          b = 0
          if use_damped_trend:
              phi = 0.999
          else:
              phi = 1.0
      else:
          adj_beta = 0
          beta = None
          b = None
          phi = None
          use_damped_trend = False
  
      gamma_one_v = np.zeros(len(k_vector))
      gamma_two_v = np.zeros(len(k_vector))
      s_vector = np.zeros(2 * np.sum(k_vector))
  
      if ar_coeffs is not None:
          d_vector = np.zeros(len(ar_coeffs))
      else:
          d_vector = None
      if ma_coeffs is not None:
          epsilon_vector = np.zeros(len(ma_coeffs))
      else:
          epsilon_vector = None
      return (
          alpha,
          adj_beta,
          beta,
          b,
          phi,
          gamma_one_v,
          gamma_two_v,
          s_vector,
          d_vector,
          epsilon_vector,
      )
  
imports: import warnings
  from functools import partial
  import numpy
  import pandas
  import scipy.linalg
  import statsmodels.api
  from numba import njit
  from numpy.polynomial.polynomial import Polynomial
  from scipy.special import inv_boxcox
  from scipy.stats import boxcox
  from scipy.optimize import minimize
  from statsmodels.tsa.stattools import adfuller
  from .arima import auto_arima_f
  from .utils import NOGIL, CACHE
label_desc: Use image node_id 2 for calling a global function with example usage: initial_parameters(seasonal_periods, k_vector, use_trend, use_damped_trend, ar_coeffs, ma_coeffs) and returns: alpha,  adj_beta,  beta,  b,  phi,  gamma_one_v,  gamma_two_v,  s_vector,  d_vector,  epsilon_vector