def predictions_to_pred(self, predictions):
        """
        Convert predictions to internal Lane structure for evaluation.
        """
        lanes = []
        for lane in predictions:
            lane_xs = lane[6:].clone()
            start = min(
                max(0, int(round(lane[2].item() * self.n_strips))),
                self.n_strips)
            length = int(round(lane[5].item()))
            end = start + length - 1
            end = min(end, len(self.prior_ys) - 1)
            if start > 0:
                mask = ((lane_xs[:start] >= 0.) &
                        (lane_xs[:start] <= 1.)).cpu().detach().numpy()[::-1]
                mask = ~((mask.cumprod()[::-1]).astype(np.bool_))
                lane_xs[:start][mask] = -2
            if end < len(self.prior_ys) - 1:
                lane_xs[end + 1:] = -2

            lane_ys = self.prior_ys[lane_xs >= 0].clone()
            lane_xs = lane_xs[lane_xs >= 0]
            lane_xs = lane_xs.flip(axis=0).astype('float64')
            lane_ys = lane_ys.flip(axis=0)

            lane_ys = (lane_ys *
                       (self.ori_img_h - self.cut_height) + self.cut_height
                       ) / self.ori_img_h
            if len(lane_xs) <= 1:
                continue
            points = paddle.stack(
                x=(lane_xs.reshape([-1, 1]), lane_ys.reshape([-1, 1])),
                axis=1).squeeze(axis=2)
            lane = Lane(
                points=points.cpu().numpy(),
                metadata={
                    'start_x': lane[3],
                    'start_y': lane[2],
                    'conf': lane[1]
                })
            lanes.append(lane)
        return lanes

        Convert predictions to internal Lane structure for evaluation.
        
deploy/python/clrnet_postprocess.py