code: def initparamces(
      alpha_0: float,
      alpha_1: float,
      beta_0: float,
      beta_1: float,
      seasontype: str,
  ):
      if np.isnan(alpha_0):
          alpha_0 = 1.3
          optimize_alpha_0 = 1
      else:
          optimize_alpha_0 = 0
      if np.isnan(alpha_1):
          alpha_1 = 1.0
          optimize_alpha_1 = 1
      else:
          optimize_alpha_1 = 0
      if seasontype == "P":
          if np.isnan(beta_0):
              beta_0 = 0.1
              optimize_beta_0 = 1
          else:
              optimize_beta_0 = 0
          beta_1 = np.nan  # no optimize
          optimize_beta_1 = 0
      elif seasontype == "F":
          if np.isnan(beta_0):
              beta_0 = 1.3
              optimize_beta_0 = 1
          else:
              optimize_beta_0 = 0
          if np.isnan(beta_1):
              beta_1 = 1.0
              optimize_beta_1 = 1
          else:
              optimize_beta_1 = 0
      else:
          # no optimize
          optimize_beta_0 = 0
          optimize_beta_1 = 0
          beta_0 = np.nan
          beta_1 = np.nan
      return {
          "alpha_0": alpha_0,
          "optimize_alpha_0": optimize_alpha_0,
          "alpha_1": alpha_1,
          "optimize_alpha_1": optimize_alpha_1,
          "beta_0": beta_0,
          "optimize_beta_0": optimize_beta_0,
          "beta_1": beta_1,
          "optimize_beta_1": optimize_beta_1,
      }
  
imports: import math
  from typing import Tuple
  import numpy
  from numba import njit
  from statsmodels.tsa.seasonal import seasonal_decompose
  from .ets import restrict_to_bounds, results
  from .utils import CACHE, NOGIL
label_desc: Use image node_id 6 for calling a global function with example usage: initparamces(alpha_0, alpha_1, beta_0, beta_1, seasontype) and returns: dict