docstr: Compute a signal prediction given spherical harmonic coefficients
  for the provided GradientTable class instance.
  
  Parameters
  ----------
  params : 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. Use the
      model's gradient table by default.
  S0 : ndarray or float
      The non diffusion-weighted signal value.
code: def predict(self, params, gtab=None, S0=None):
      """Compute a signal prediction given spherical harmonic coefficients
      for the provided GradientTable class instance.
  
      Parameters
      ----------
      params : 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. Use the
          model's gradient table by default.
      S0 : ndarray or float
          The non diffusion-weighted signal value.
      """
      if gtab is None or gtab is self.gtab:
          X = self._X
      else:
          iso = self.response.iso
          B, m, n = multi_tissue_basis(gtab, self.sh_order, iso)
          multiplier_matrix = _inflate_response(
              self.response, gtab, n, self.delta
          )
          X = B * multiplier_matrix
  
      scaling = 1.0
      if S0 and S0 != 1.0:  # The S0=1. case comes from fit.predict().
          raise NotImplementedError
          # This case is not implemented yet because it would require to have
          # access to volume fractions (vf) from the fit. The following code
          # gives an idea of how to use this with S0 and vf. It could also be
          # calculated externally and used as scaling = S0.
          # response_scaling = np.ndarray(params.shape[0:3])
          # response_scaling[...] = (vf[..., 0] * self.response.S0[0]
          #                          + vf[..., 1] * self.response.S0[1]
          #                          + vf[..., 2] * self.response.S0[2])
          # scaling = np.where(response_scaling > 1, S0 / response_scaling, 0)
          # scaling = np.expand_dims(scaling, 3)
          # scaling = np.repeat(scaling, len(gtab.bvals), axis=3)
  
      pred_sig = scaling * np.dot(params, X.T)
      return pred_sig
  
imports: from packaging.version import Version
  import numbers
  import warnings
  import numpy
  from dipy.core import geometry
  from dipy.core.gradients import GradientTable, gradient_table, unique_bvals_tolerance, get_bval_indices
  from dipy.data import default_sphere
  from dipy.reconst import shm
  from dipy.reconst.csdeconv import response_from_mask_ssst
  from dipy.reconst.dti import TensorModel, fractional_anisotropy, mean_diffusivity
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.reconst.utils import _roi_in_volume, _mask_from_roi
  from dipy.sims.voxel import single_tensor
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 2 for calling the MultiShellDeconvModel obj's underlying member method code with example usage: obj.predict(params, gtab, S0) and returns: pred_sig