docstr: Calculates the fODF for a given discrete sphere.
  
  Parameters
  ----------
  gtab : GradientTable, optional
      gradient directions and bvalues container class.
  S0 : float, optional
      the signal at b-value=0
docstr_tok: ["Calculates","the","fODF","for","a","given","discrete","sphere",".","Parameters","--","--","--","--","--","gtab",":","GradientTable",",","optional","gradient","directions","and","bvalues","container","class",".","S0",":","float",",","optional","the","signal","at","b-value=0"]
code: def predict(self, gtab=None, S0=1.0):
      r"""Calculates the fODF for a given discrete sphere.
  
      Parameters
      ----------
      gtab : GradientTable, optional
          gradient directions and bvalues container class.
      S0 : float, optional
          the signal at b-value=0
  
      """
      if gtab is None:
          gtab = self.gtab
  
      M_diff = forecast_matrix(
          self.sh_order, self.d_par, self.d_perp, gtab.bvals
      )
  
      rho = rho_matrix(self.sh_order, gtab.bvecs)
      M = M_diff * rho
      S = S0 * np.dot(M, self._sh_coef)
  
      return S
  
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 5 for calling the ForecastFit obj's underlying member method code with example usage: obj.predict(gtab, S0) and returns: S