class_docstr: A fit object for representing the isotropic signal as the mean of the
  diffusion-weighted signal.
docstr: Initialize an IsotropicFit object.
  
  Parameters
  ----------
  model : IsotropicModel class instance
  params : ndarray
      The mean isotropic model parameters (the mean diffusion-weighted
      signal in each voxel).
  n_vox : int
      The number of voxels for which the fit was done.
docstr_tok: ["Initialize","an","IsotropicFit","object",".","Parameters","--","--","--","--","--","model",":","IsotropicModel","class","instance","params",":","ndarray","The","mean","isotropic","model","parameters","(","the","mean","diffusion-weighted","signal","in","each","voxel",")",".","n_vox",":","int","The","number","of","voxels","for","which","the","fit","was","done","."]
code: def __init__(self, model, params):
      """Initialize an IsotropicFit object.
  
      Parameters
      ----------
      model : IsotropicModel class instance
      params : ndarray
          The mean isotropic model parameters (the mean diffusion-weighted
          signal in each voxel).
      n_vox : int
          The number of voxels for which the fit was done.
  
      """
      super().__init__(self, model)
      self.model = model
      self.params = params
  
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 IsotropicFit object from inherited base classes: ReconstFit with example: obj = IsotropicFit(model, params)