code: def _compute_sigmah(pf, h, sigma, cvals):
      theta = np.full(h, np.nan)
      theta[0] = pf[0] ** 2
  
      for k in range(1, h):
          sum_val = 0
          for j in range(1, k + 1):
              val = cvals[j - 1] ** 2 * theta[k - j]
              sum_val = sum_val + val
          theta[k] = pf[k] ** 2 + sigma * sum_val
  
      sigmah = np.full(h, np.nan)
      for k in range(0, h):
          sigmah[k] = (1 + sigma) * theta[k] - pf[k] ** 2
  
      return sigmah
  
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 23 for calling a global function with example usage: _compute_sigmah(pf, h, sigma, cvals) and returns: sigmah