code: def evaluate(self, x, y, flux, x_0, y_0):
          
  
          # NOTE: this is needed because the PSF photometry routines input
          # length-1 values instead of scalars.  TODO: fix the photometry
          # routines.
          if not np.isscalar(x_0):
              x_0 = x_0[0]
          if not np.isscalar(y_0):
              y_0 = y_0[0]
  
          if (x_0 < self._xgrid_min or x_0 > self._xgrid_max or
                  y_0 < self._ygrid_min or y_0 > self._ygrid_max):
  
              # position is outside of the grid, so simply use the
              # closest reference PSF
              self._ref_indices = np.argsort(np.hypot(self._grid_xpos - x_0,
                                                      self._grid_ypos - y_0))[0]
              self._psf_interp = self.data[self._ref_indices, :, :]
          else:
              # find the four bounding reference PSFs and interpolate
              self._ref_indices = self._find_bounding_points(x_0, y_0)
              xyref = np.array(self.grid_xypos)[self._ref_indices]
              psfs = self.data[self._ref_indices, :, :]
  
              self._psf_interp = self._bilinear_interp(xyref, psfs, x_0, y_0)
  
          # now evaluate the PSF at the (x_0, y_0) subpixel position on
          # the input (x, y) values
          psfmodel = FittableImageModel(self._psf_interp,
                                        oversampling=self.oversampling)
  
          return psfmodel.evaluate(x, y, flux, x_0, y_0)
docs: Evaluate the `GriddedPSFModel` for the input parameters.