class_docstr: A fit to the ExponentialIsotropicModel object, based on data.
docstr: Predict the isotropic signal, based on a gradient table. In this case,
  the prediction will be for an exponential decay with the mean
  diffusivity derived from the data that was fit.
  
  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","prediction","will","be","for","an","exponential","decay","with","the","mean","diffusivity","derived","from","the","data","that","was","fit",".","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 prediction will be for an exponential decay with the mean
      diffusivity derived from the data that was fit.
  
      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 np.exp(
              -gtab.bvals[~gtab.b0s_mask]
              * (
                  np.zeros(np.sum(~gtab.b0s_mask))
                  + self.params[..., np.newaxis]
              )
          )
      else:
          return np.exp(
              -gtab.bvals[~gtab.b0s_mask]
              * (
                  np.zeros(
                      (self.params.shape[0], np.sum(~gtab.b0s_mask))
                  )
                  + self.params[..., np.newaxis]
              )
          )
  
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 for calling the ExponentialIsotropicFit obj's underlying member method code with example usage: obj.predict(gtab) and returns: np, np