class_docstr: A base-class for the representation of isotropic signals.
  
  The default behavior, suitable for single b-value data is to calculate the
  mean in each voxel as an estimate of the signal that does not depend on
  direction.
docstr: Fit an IsotropicModel.
  
  This boils down to finding the mean diffusion-weighted signal in each
  voxel
  
  Parameters
  ----------
  data : ndarray
  
  Returns
  -------
  IsotropicFit class instance.
docstr_tok: ["Fit","an","IsotropicModel",".","This","boils","down","to","finding","the","mean","diffusion-weighted","signal","in","each","voxel","Parameters","--","--","--","--","--","data",":","ndarray","Returns","--","--","--","-","IsotropicFit","class","instance","."]
code: def fit(self, data, mask=None, **kwargs):
      """Fit an IsotropicModel.
  
      This boils down to finding the mean diffusion-weighted signal in each
      voxel
  
      Parameters
      ----------
      data : ndarray
  
      Returns
      -------
      IsotropicFit class instance.
  
      """
      # This returns as a 2D thing:
      params = np.mean(_to_fit_iso(data, self.gtab, mask=mask), -1)
      if mask is None:
          params = np.reshape(params, data.shape[:-1])
      else:
          out_params = np.zeros(data.shape[:-1])
          out_params[mask] = params
          params = out_params
      return IsotropicFit(self, 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 2 for calling the IsotropicModel obj's underlying member method code with example usage: obj.fit(data, mask) and returns: IsotropicFit