code: def lb_forecast(sh_order):
      r"""Returns the Laplace-Beltrami regularization matrix for FORECAST"""
      n_c = int((sh_order + 1) * (sh_order + 2) / 2)
      diag_lb = np.zeros(n_c)
      counter = 0
      for l in range(0, sh_order + 1, 2):
          stop = 2 * l + 1 + counter
          diag_lb[counter:stop] = (l * (l + 1)) ** 2
          counter = stop
  
      return np.diag(diag_lb)
  
imports: from warnings import warn
  import numpy
  from dipy.reconst.cache import Cache
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.csdeconv import csdeconv
  from dipy.reconst.shm import real_sh_descoteaux_from_index
  from scipy.special import gamma, hyp1f1
  from dipy.core.geometry import cart2sphere
  from dipy.data import default_sphere
  from dipy.reconst.odf import OdfModel, OdfFit
  from scipy.optimize import leastsq
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 6 for calling a global function with example usage: lb_forecast(sh_order) and returns: np