code: def _compute_sigmah(obj, h):
      """
      Computes the sigmah requiered for prediction intervals
      """
      var_mult = np.zeros(h)
      var_mult[0] = 1
      if h > 1:
          for j in range(1, h):
              if j == 1:
                  f_running = np.eye(obj["F"].shape[1])
              else:
                  f_running = np.dot(f_running, obj["F"])
              cj = np.dot(
                  np.dot(obj["w_transpose"][0], f_running),
                  obj["g"][:, 0],
              )
              var_mult[j] = var_mult[j - 1] + cj**2
      sigma2h = obj["sigma2"] * var_mult
      sigmah = np.sqrt(sigma2h)
      return sigmah
  
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 18 for calling a global function with example usage: _compute_sigmah(obj, h) and returns: sigmah