code: def forecast(l, b, s, m, trend, season, phi, f, h):
      # f is modified and it is mutable
      phistar = phi
      # forecasts
      for i in range(h):
          # print(phistar)
          if trend == NONE:
              f[i] = l
          elif trend == ADD:
              f[i] = l + phistar * b
          elif b < 0:
              f[i] = NA
          else:
              f[i] = l * math.pow(b, phistar)
          j = m - 1 - i
          while j < 0:
              j += m
          if season == ADD:
              f[i] = f[i] + s[j]
          elif season == MULT:
              f[i] = f[i] * s[j]
          if i < h - 1:
              if math.fabs(phi - 1.0) < TOL:
                  phistar = phistar + 1.0
              else:
                  phistar = phistar + math.pow(phi, i + 1)
  
imports: import math
  from collections import namedtuple
  from typing import Tuple
  import numpy
  from numba import njit
  from numba.typed import List
  from statsmodels.tsa.seasonal import seasonal_decompose
  from .utils import _calculate_intervals, CACHE, NOGIL
label_desc: Use image node_id 2 for calling a global function with example usage: forecast(l, b, s, m, trend, season, phi, f, h) without return types