def start_end_points(self):
        """
        return torch.Tensor([N,4]), in xstart, ystart, xend, yend form
        """
        assert len(self.instance_list) != 0
        instance_se_points_list = []
        for instance in self.instance_list:
            se_points = []
            se_points.extend(instance.coords[0])
            se_points.extend(instance.coords[-1])
            instance_se_points_list.append(se_points)
        instance_se_points_array = np.array(instance_se_points_list)
        instance_se_points_tensor = to_tensor(instance_se_points_array)
        instance_se_points_tensor = instance_se_points_tensor.to(
                                dtype=torch.float32)
        instance_se_points_tensor[:,0] = torch.clamp(instance_se_points_tensor[:,0], min=-self.max_x,max=self.max_x)
        instance_se_points_tensor[:,1] = torch.clamp(instance_se_points_tensor[:,1], min=-self.max_y,max=self.max_y)
        instance_se_points_tensor[:,2] = torch.clamp(instance_se_points_tensor[:,2], min=-self.max_x,max=self.max_x)
        instance_se_points_tensor[:,3] = torch.clamp(instance_se_points_tensor[:,3], min=-self.max_y,max=self.max_y)
        return instance_se_points_tensor

        return torch.Tensor([N,4]), in xstart, ystart, xend, yend form
        
projects/mmdet3d_plugin/datasets/nuscenes_vad_dataset.py