docstr: Fits the model to diffusion data and returns the model fit.
  
  Sometimes the solving process of some voxels can end in a SolverError
  from cvxpy. This might be attributed to the response functions not
  being tuned properly, as the solving process is very sensitive to it.
  The method will fill the problematic voxels with a NaN value, so that
  it is traceable. The user should check for the number of NaN values and
  could then fill the problematic voxels with zeros, for example.
  Running a fit again only on those problematic voxels can also work.
  
  Parameters
  ----------
  data : ndarray
      The diffusion data to fit the model on.
  verbose : bool (optional)
      Whether to show warnings when a SolverError appears or not.
      Default: True
code: def fit(self, data, verbose=True):
      """Fits the model to diffusion data and returns the model fit.
  
      Sometimes the solving process of some voxels can end in a SolverError
      from cvxpy. This might be attributed to the response functions not
      being tuned properly, as the solving process is very sensitive to it.
      The method will fill the problematic voxels with a NaN value, so that
      it is traceable. The user should check for the number of NaN values and
      could then fill the problematic voxels with zeros, for example.
      Running a fit again only on those problematic voxels can also work.
  
      Parameters
      ----------
      data : ndarray
          The diffusion data to fit the model on.
      verbose : bool (optional)
          Whether to show warnings when a SolverError appears or not.
          Default: True
      """
      coeff = self.fitter(data)
      if verbose:
          if np.isnan(coeff[..., 0]):
              msg = """Voxel could not be solved properly and ended up with a
                  SolverError. Proceeding to fill it with NaN values.
                  """
              warnings.warn(msg, UserWarning)
  
      return MSDeconvFit(self, coeff, None)
  
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 3 for calling the MultiShellDeconvModel obj's underlying member method code with example usage: obj.fit(data, verbose) and returns: MSDeconvFit