code: def results(self):
          
  
          # Add all saved samples to the results.
          if self.save_samples:
              with warnings.catch_warnings():
                  warnings.simplefilter("ignore")
                  results = [('nlive', self.nlive),
                             ('niter', self.it - 1),
                             ('ncall', np.array(self.saved_nc)),
                             ('eff', self.eff),
                             ('samples', np.array(self.saved_v)),
                             ('samples_id', np.array(self.saved_id)),
                             ('samples_it', np.array(self.saved_it)),
                             ('samples_u', np.array(self.saved_u)),
                             ('logwt', np.array(self.saved_logwt)),
                             ('logl', np.array(self.saved_logl)),
                             ('logvol', np.array(self.saved_logvol)),
                             ('logz', np.array(self.saved_logz)),
                             ('logzerr', np.sqrt(np.array(self.saved_logzvar))),
                             ('information', np.array(self.saved_h))]
          else:
              raise ValueError("You didn't save any samples!")
  
          # Add any saved bounds (and ancillary quantities) to the results.
          if self.save_bounds:
              results.append(('bound', copy.deepcopy(self.bound)))
              results.append(('bound_iter',
                              np.array(self.saved_bounditer, dtype='int')))
              results.append(('samples_bound',
                              np.array(self.saved_boundidx, dtype='int')))
              results.append(('scale', np.array(self.saved_scale)))
  
          return Results(results)
docs: Saved results from the nested sampling run. If bounding
          distributions were saved, those are also returned.