docstr: Compute a signal prediction given spherical harmonic coefficients
  for the provided GradientTable class instance.
  
  Parameters
  ----------
  sh_coeff : ndarray
      The spherical harmonic representation of the FOD from which to make
      the signal prediction.
  gtab : GradientTable
      The gradients for which the signal will be predicted. Uses the
      model's gradient table by default.
  S0 : ndarray or float
      The non diffusion-weighted signal value.
  
  Returns
  -------
  pred_sig : ndarray
      The predicted signal.
docstr_tok: ["Compute","a","signal","prediction","given","spherical","harmonic","coefficients","for","the","provided","GradientTable","class","instance",".","Parameters","--","--","--","--","--","sh_coeff",":","ndarray","The","spherical","harmonic","representation","of","the","FOD","from","which","to","make","the","signal","prediction",".","gtab",":","GradientTable","The","gradients","for","which","the","signal","will","be","predicted",".","Uses","the","model","'s","gradient","table","by","default",".","S0",":","ndarray","or","float","The","non","diffusion-weighted","signal","value",".","Returns","--","--","--","-","pred_sig",":","ndarray","The","predicted","signal","."]
code: def predict(self, sh_coeff, gtab=None, S0=1.0):
      """Compute a signal prediction given spherical harmonic coefficients
      for the provided GradientTable class instance.
  
      Parameters
      ----------
      sh_coeff : ndarray
          The spherical harmonic representation of the FOD from which to make
          the signal prediction.
      gtab : GradientTable
          The gradients for which the signal will be predicted. Uses the
          model's gradient table by default.
      S0 : ndarray or float
          The non diffusion-weighted signal value.
  
      Returns
      -------
      pred_sig : ndarray
          The predicted signal.
  
      """
      if gtab is None or gtab is self.gtab:
          SH_basis = self.B_dwi
          gtab = self.gtab
      else:
          x, y, z = gtab.gradients[~gtab.b0s_mask].T
          r, theta, phi = cart2sphere(x, y, z)
          SH_basis, m, n = real_sh_descoteaux(self.sh_order, theta, phi)
  
      # Because R is diagonal, the matrix multiply is written as a multiply
      predict_matrix = SH_basis * self.R.diagonal()
      S0 = np.asarray(S0)[..., None]
      scaling = S0 / self.response_scaling
      # This is the key operation: convolve and multiply by S0:
      pre_pred_sig = scaling * np.dot(sh_coeff, predict_matrix.T)
  
      # Now put everything in its right place:
      pred_sig = np.zeros(
          pre_pred_sig.shape[:-1] + (gtab.bvals.shape[0],)
      )
      pred_sig[..., ~gtab.b0s_mask] = pre_pred_sig
      pred_sig[..., gtab.b0s_mask] = S0
  
      return pred_sig
  
imports: import warnings
  import numpy
  import numbers
  from scipy.integrate import quad
  from scipy.special import lpn, gamma
  import scipy.linalg
  import scipy.linalg.lapack
  from dipy.data import small_sphere, get_sphere, default_sphere
  from dipy.core.geometry import cart2sphere
  from dipy.core.ndindex import ndindex
  from dipy.sims.voxel import single_tensor
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.dti import TensorModel, fractional_anisotropy
  from dipy.reconst.shm import sph_harm_ind_list, real_sh_descoteaux_from_index, sph_harm_lookup, lazy_index, SphHarmFit, real_sh_descoteaux, sh_to_rh, forward_sdeconv_mat, SphHarmModel
  from dipy.reconst.utils import _roi_in_volume, _mask_from_roi
  from dipy.direction.peaks import peaks_from_model
  from dipy.core.geometry import vec2vec_rotmat
  from dipy.utils.deprecator import deprecate_with_version, deprecated_params
label_desc: Use image node_id 3 for calling the ConstrainedSphericalDeconvModel obj's underlying member method code with example usage: obj.predict(sh_coeff, gtab, S0) and returns: pred_sig