code: def find_expected_error(self, delta_params='calc', adjust=True):
          
          expected_error = super(LMGlobals, self).find_expected_error(
                  delta_params=delta_params)
          if adjust:
              #adjust for num_pix
              derr = (expected_error - self.error) * (self.state.residuals.size /
                      float(self.num_pix))
              expected_error = self.error + derr
          return expected_error
docs: Returns the error expected after an update if the model were linear.
  
          Parameters
          ----------
              delta_params : {numpy.ndarray, 'calc', or 'perfect'}, optional
                  The relative change in parameters. If 'calc', uses update
                  calculated from the current damping, J, etc; if 'perfect',
                  uses the update calculated with zero damping.
  
          Returns
          -------
              numpy.float64
                  The expected error after the update with `delta_params`