docstr: Abstract class which holds the fit result of MultiShellDeconvModel.
  Inherits the SphHarmFit which fits the diffusion data to a spherical
  harmonic model.
  
  Parameters
  ----------
  model: object
      MultiShellDeconvModel
  coeff : array
      Spherical harmonic coefficients for the ODF.
  mask: ndarray
      Mask for fitting
code: def __init__(self, model, coeff, mask):
      """
      Abstract class which holds the fit result of MultiShellDeconvModel.
      Inherits the SphHarmFit which fits the diffusion data to a spherical
      harmonic model.
  
      Parameters
      ----------
      model: object
          MultiShellDeconvModel
      coeff : array
          Spherical harmonic coefficients for the ODF.
      mask: ndarray
          Mask for fitting
      """
      self._shm_coef = coeff
      self.mask = mask
      self.model = model
  
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 1 to create a new MSDeconvFit object from inherited base classes: shm with example: obj = MSDeconvFit(model, coeff, mask)