docstr: Initialize a SparseFascicleFit class instance
  
  Parameters
  ----------
  model : a SparseFascicleModel object.
  
  beta : ndarray
      The parameters of fit to data.
  
  S0 : ndarray
      The mean non-diffusion-weighted signal.
  
  iso : IsotropicFit class instance
      A representation of the isotropic signal, together with parameters
      of the isotropic signal in each voxel, that is capable of
      deriving/predicting an isotropic signal, based on a gradient-table.
docstr_tok: ["Initialize","a","SparseFascicleFit","class","instance","Parameters","--","--","--","--","--","model",":","a","SparseFascicleModel","object",".","beta",":","ndarray","The","parameters","of","fit","to","data",".","S0",":","ndarray","The","mean","non-diffusion-weighted","signal",".","iso",":","IsotropicFit","class","instance","A","representation","of","the","isotropic","signal",",","together","with","parameters","of","the","isotropic","signal","in","each","voxel",",","that","is","capable","of","deriving\/predicting","an","isotropic","signal",",","based","on","a","gradient-table","."]
code: def __init__(self, model, beta, S0, iso):
      """
      Initialize a SparseFascicleFit class instance
  
      Parameters
      ----------
      model : a SparseFascicleModel object.
  
      beta : ndarray
          The parameters of fit to data.
  
      S0 : ndarray
          The mean non-diffusion-weighted signal.
  
      iso : IsotropicFit class instance
          A representation of the isotropic signal, together with parameters
          of the isotropic signal in each voxel, that is capable of
          deriving/predicting an isotropic signal, based on a gradient-table.
      """
      super().__init__(self, model)
  
      self.model = model
      self.beta = beta
      self.S0 = S0
      self.iso = iso
  
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 1 to create a new SparseFascicleFit object from inherited base classes: ReconstFit with example: obj = SparseFascicleFit(model, beta, S0, iso)