code: def _fit_solver2voxels(
      self, isopredict, vox_data, vox, parallel=False
  ):
      # In voxels in which S0 is 0, we just want to keep the
      # parameters at all-zeros, and avoid nasty sklearn errors:
      if not (np.any(~np.isfinite(vox_data)) or np.all(vox_data == 0)):
          with warnings.catch_warnings():
              warnings.simplefilter("ignore")
  
              if parallel:
                  coef = {
                      vox: self.solver.fit(
                          self.design_matrix, vox_data - isopredict[vox]
                      ).coef_
                  }
              else:
                  coef = self.solver.fit(
                      self.design_matrix, vox_data - isopredict[vox]
                  ).coef_
      else:
          if parallel:
              return {vox: np.zeros(self.design_matrix.shape[-1])}
          else:
              return np.zeros(self.design_matrix.shape[-1])
      return coef
  
imports: import warnings
  import numpy
  import gc
  from collections import OrderedDict
  from dipy.utils.optpkg import optional_package
  from dipy.utils.multiproc import determine_num_processes
  import dipy.core.gradients
  import dipy.core.optimize
  import dipy.sims.voxel
  import dipy.data
  from dipy.reconst.base import ReconstModel, ReconstFit
  from dipy.reconst.cache import Cache
  from dipy.core.onetime import auto_attr
label_desc: Use image node_id 3 for calling the SparseFascicleModel obj's underlying member method code with example usage: obj._fit_solver2voxels(isopredict, vox_data, vox, parallel) and returns: coef, dict, np