class_docstr: A fit object for representing the isotropic signal as the mean of the
  diffusion-weighted signal.
docstr: Predict the isotropic signal.
  
  Based on a gradient table. In this case, the (naive!) prediction will
  be the mean of the diffusion-weighted signal in the voxels.
  
  Parameters
  ----------
  gtab : a GradientTable class instance (optional)
      Defaults to use the gtab from the IsotropicModel from which this
      fit was derived.
docstr_tok: ["Predict","the","isotropic","signal",".","Based","on","a","gradient","table",".","In","this","case",",","the","(","naive","!",")","prediction","will","be","the","mean","of","the","diffusion-weighted","signal","in","the","voxels",".","Parameters","--","--","--","--","--","gtab",":","a","GradientTable","class","instance","(","optional",")","Defaults","to","use","the","gtab","from","the","IsotropicModel","from","which","this","fit","was","derived","."]
code: def predict(self, gtab=None):
      """Predict the isotropic signal.
  
      Based on a gradient table. In this case, the (naive!) prediction will
      be the mean of the diffusion-weighted signal in the voxels.
  
      Parameters
      ----------
      gtab : a GradientTable class instance (optional)
          Defaults to use the gtab from the IsotropicModel from which this
          fit was derived.
  
      """
      if gtab is None:
          gtab = self.model.gtab
      if len(self.params.shape) == 0:
          return self.params[..., np.newaxis] + np.zeros(
              np.sum(~gtab.b0s_mask)
          )
      else:
          return self.params[..., np.newaxis] + np.zeros(
              self.params.shape + (np.sum(~gtab.b0s_mask),)
          )
  
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 IsotropicFit obj's underlying member method code with example usage: obj.predict(gtab) and returns: unknown, unknown