How to use the proposal.ProposalLayer function in proposal

def __init__(self, args):
            super(Model, self).__init__()

            print("using backbone", args.backbone)
            if args.backbone == "vgg16_torch":
                self.feature_extractor = CNN()

            elif args.backbone == "vgg16_longcw":
                self.feature_extractor = VGG16()
                self.feature_extractor.load_from_npy_file('../input/pretrained_model/VGG_imagenet.npy')

            self.rpn = RPN()
            self.fasterrcnn = FasterRcnn()
            self.proplayer = ProposalLayer(args=args)
            self.roipool = ROIpooling()

def __init__(self, args):
            super(Model, self).__init__()

            print("using backbone", args.backbone)
            if args.backbone == "vgg16_torch":
                self.feature_extractor = CNN()

            elif args.backbone == "vgg16_longcw":
                self.feature_extractor = VGG16()
                self.feature_extractor.load_from_npy_file('../input/pretrained_model/VGG_imagenet.npy')

            self.rpn = RPN()
            self.fasterrcnn = FasterRcnn()
            self.proplayer = ProposalLayer(args=args)
            self.roipool = ROIpooling()

print("features : {} ".format(features.size()))

    # RPN test

    rpn = RPN()
    rpn_bbox_pred, rpn_cls_prob = rpn(features)
    print("rpn_bbox_pred : {}, rpn_cls_prob : {}".format(rpn_bbox_pred.size(), rpn_cls_prob.size())) # torch.Size([1, 36, 62, 37]) torch.Size([1, 18, 62, 37])

    # get_achors test

    all_anchors = get_anchors(features, anchor)
    print("all_anchors : {}".format(all_anchors.shape))

    # proposal layer test

    proplayer = ProposalLayer(rpn_bbox_pred, rpn_cls_prob, all_anchors, im_info=image_info, args=args)
    proposals, scores = proplayer.proposal()
    print("proposals : {}, scores : {}".format(proposals.shape, scores.shape))
    print(proposals.astype("int"))

    # rpn_target test
    rpn_labels, rpn_bbox_targets = rpn_targets(all_anchors, image, gt_boxes, args)
    print("rpn_labels : {}, bbox_target : {}".format(rpn_labels.shape, rpn_bbox_targets.shape)) # (20646,) (20646, 4)

    # gt_boxes도 추가해줘야 해서 targets을 먼저 구한다.
    # frcnn_targets test
    frcnn_labels, rois, frcnn_bbox_targets = frcnn_targets(proposals, gt_boxes, args)
    print("frcnn_labels : {}, rois : {}, frcnn_bbox_targets : {}".format(frcnn_labels.shape, rois.shape, frcnn_bbox_targets.shape))

    # ROIpooling test
    roipool = ROIpooling()
    rois_features = roipool(features, rois)