def __call__(self, bboxes, score, background_label=-1):
        """
        bboxes (Tensor|List[Tensor]): 1. (Tensor) Predicted bboxes with shape 
                                         [N, M, 4], N is the batch size and M
                                         is the number of bboxes
                                      2. (List[Tensor]) bboxes and bbox_num,
                                         bboxes have shape of [M, C, 4], C
                                         is the class number and bbox_num means
                                         the number of bboxes of each batch with
                                         shape [N,] 
        score (Tensor): Predicted scores with shape [N, C, M] or [M, C]
        background_label (int): Ignore the background label; For example, RCNN
                                is num_classes and YOLO is -1. 
        """
        kwargs = self.__dict__.copy()
        if isinstance(bboxes, tuple):
            bboxes, bbox_num = bboxes
            kwargs.update({'rois_num': bbox_num})
        if background_label > -1:
            kwargs.update({'background_label': background_label})
        kwargs.pop('trt')
        kwargs.pop('cpu')

        # TODO(wangxinxin08): paddle version should be develop or 2.3 and above to run nms on tensorrt
        if self.trt and (int(paddle.version.major) == 0 or
                         (int(paddle.version.major) >= 2 and
                          int(paddle.version.minor) >= 3)):
            # TODO(wangxinxin08): tricky switch to run nms on tensorrt
            kwargs.update({'nms_eta': 1.1})
            bbox, bbox_num, _ = ops.multiclass_nms(bboxes, score, **kwargs)
            bbox = bbox.reshape([1, -1, 6])
            idx = paddle.nonzero(bbox[..., 0] != -1)
            bbox = paddle.gather_nd(bbox, idx)
            return bbox, bbox_num, None
        else:
            if self.cpu:
                device = paddle.device.get_device()
                paddle.set_device('cpu')
                outputs = ops.multiclass_nms(bboxes, score, **kwargs)
                paddle.set_device(device)
                return outputs
            else:
                return ops.multiclass_nms(bboxes, score, **kwargs)

        bboxes (Tensor|List[Tensor]): 1. (Tensor) Predicted bboxes with shape 
                                         [N, M, 4], N is the batch size and M
                                         is the number of bboxes
                                      2. (List[Tensor]) bboxes and bbox_num,
                                         bboxes have shape of [M, C, 4], C
                                         is the class number and bbox_num means
                                         the number of bboxes of each batch with
                                         shape [N,] 
        score (Tensor): Predicted scores with shape [N, C, M] or [M, C]
        background_label (int): Ignore the background label; For example, RCNN
                                is num_classes and YOLO is -1. 
        
ppdet/modeling/layers.py