How to use the mmdet.core.multiclass_nms function in mmdet

roi_feats = self.shared_head(roi_feats)
            cls_score, bbox_pred = self.bbox_head(roi_feats)
            bboxes, scores = self.bbox_head.get_det_bboxes(
                rois,
                cls_score,
                bbox_pred,
                img_shape,
                scale_factor,
                rescale=False,
                cfg=None)
            aug_bboxes.append(bboxes)
            aug_scores.append(scores)
        # after merging, bboxes will be rescaled to the original image size
        merged_bboxes, merged_scores = merge_aug_bboxes(
            aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
        det_bboxes, det_labels = multiclass_nms(
            merged_bboxes, merged_scores, rcnn_test_cfg.score_thr,
            rcnn_test_cfg.nms, rcnn_test_cfg.max_per_img)
        return det_bboxes, det_labels

proposals = delta2bbox(anchors, bbox_pred, self.target_means,
                                   self.target_stds, img_shape)
            if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
                maxscores, _ = scores.max(dim=1)
                _, topk_inds = maxscores.topk(cfg.nms_pre)
                proposals = proposals[topk_inds, :]
                scores = scores[topk_inds, :]
            mlvl_proposals.append(proposals)
            mlvl_scores.append(scores)
        mlvl_proposals = torch.cat(mlvl_proposals)
        if rescale:
            mlvl_proposals /= scale_factor
        mlvl_scores = torch.cat(mlvl_scores)
        padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
        mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
        det_bboxes, det_labels = multiclass_nms(mlvl_proposals, mlvl_scores,
                                                cfg.score_thr, cfg.nms,
                                                cfg.max_per_img)
        return det_bboxes, det_labels

if use_sigmoid_cls:
            max_scores, _ = cls_scores.max(dim=1)
        else:
            max_scores, _ = cls_scores[:, 1:].max(dim=1)
        _, topk_inds = max_scores.topk(nms_pre)
        priors = priors[topk_inds, :]
        bbox_preds = bbox_preds[topk_inds, :]
        cls_scores = cls_scores[topk_inds, :]
    mlvl_bboxes = delta2bbox(priors, bbox_preds, target_means,
                             target_stds, img_shape)
    if rescale:
        mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
    if use_sigmoid_cls:
        padding = cls_scores.new_zeros(cls_scores.shape[0], 1)
        cls_scores = torch.cat([padding, cls_scores], dim=1)
    det_bboxes, det_labels = multiclass_nms(
        mlvl_bboxes, cls_scores, cfg.score_thr, cfg.nms, cfg.max_per_img)
    return det_bboxes, det_labels

anchors = anchors[topk_inds, :]
                bbox_pred = bbox_pred[topk_inds, :]
                scores = scores[topk_inds, :]
            bboxes = delta2bbox(anchors, bbox_pred, self.target_means,
                                self.target_stds, img_shape)
            mlvl_bboxes.append(bboxes)
            mlvl_scores.append(scores)
        mlvl_bboxes = torch.cat(mlvl_bboxes)
        if rescale:
            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
        mlvl_scores = torch.cat(mlvl_scores)
        if self.use_sigmoid_cls:
            # Add a dummy background class to the front when using sigmoid
            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
            mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
        det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
                                                cfg.score_thr, cfg.nms,
                                                cfg.max_per_img)
        return det_bboxes, det_labels

points = points[topk_inds, :]
                bbox_pred = bbox_pred[topk_inds, :]
                scores = scores[topk_inds, :]
                centerness = centerness[topk_inds]
            bboxes = distance2bbox(points, bbox_pred, max_shape=img_shape)
            mlvl_bboxes.append(bboxes)
            mlvl_scores.append(scores)
            mlvl_centerness.append(centerness)
        mlvl_bboxes = torch.cat(mlvl_bboxes)
        if rescale:
            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
        mlvl_scores = torch.cat(mlvl_scores)
        padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
        mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
        mlvl_centerness = torch.cat(mlvl_centerness)
        det_bboxes, det_labels = multiclass_nms(
            mlvl_bboxes,
            mlvl_scores,
            cfg.score_thr,
            cfg.nms,
            cfg.max_per_img,
            score_factors=mlvl_centerness)
        return det_bboxes, det_labels

if bbox_pred is not None:
            bboxes = delta2bbox(rois[:, 1:], bbox_pred, self.target_means,
                                self.target_stds, img_shape)
        else:
            bboxes = rois[:, 1:].clone()
            if img_shape is not None:
                bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1)
                bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1)

        if rescale:
            bboxes /= scale_factor

        if cfg is None:
            return bboxes, scores
        else:
            det_bboxes, det_labels = multiclass_nms(bboxes, scores,
                                                    cfg.score_thr, cfg.nms,
                                                    cfg.max_per_img)

            return det_bboxes, det_labels

cls_score = sum(ms_scores) / float(len(ms_scores))
            bboxes, scores = self.bbox_head[-1].get_det_bboxes(
                rois,
                cls_score,
                bbox_pred,
                img_shape,
                scale_factor,
                rescale=False,
                cfg=None)
            aug_bboxes.append(bboxes)
            aug_scores.append(scores)

        # after merging, bboxes will be rescaled to the original image size
        merged_bboxes, merged_scores = merge_aug_bboxes(
            aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
        det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
                                                rcnn_test_cfg.score_thr,
                                                rcnn_test_cfg.nms,
                                                rcnn_test_cfg.max_per_img)

        bbox_result = bbox2result(det_bboxes, det_labels,
                                  self.bbox_head[-1].num_classes)

        if self.with_mask:
            if det_bboxes.shape[0] == 0:
                segm_result = [[]
                               for _ in range(self.mask_head[-1].num_classes -
                                              1)]
            else:
                aug_masks = []
                aug_img_metas = []
                for x, img_meta in zip(self.extract_feats(imgs), img_metas):

x1 = bboxes[:, 0].clamp(min=0, max=img_shape[1])
            y1 = bboxes[:, 1].clamp(min=0, max=img_shape[0])
            x2 = bboxes[:, 2].clamp(min=0, max=img_shape[1])
            y2 = bboxes[:, 3].clamp(min=0, max=img_shape[0])
            bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
            mlvl_bboxes.append(bboxes)
            mlvl_scores.append(scores)
        mlvl_bboxes = torch.cat(mlvl_bboxes)
        if rescale:
            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
        mlvl_scores = torch.cat(mlvl_scores)
        if self.use_sigmoid_cls:
            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
            mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
        if nms:
            det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
                                                    cfg.score_thr, cfg.nms,
                                                    cfg.max_per_img)
            return det_bboxes, det_labels
        else:
            return mlvl_bboxes, mlvl_scores

x1 = bboxes[:, 0].clamp(min=0, max=img_shape[1])
            y1 = bboxes[:, 1].clamp(min=0, max=img_shape[0])
            x2 = bboxes[:, 2].clamp(min=0, max=img_shape[1])
            y2 = bboxes[:, 3].clamp(min=0, max=img_shape[0])
            bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
            mlvl_bboxes.append(bboxes)
            mlvl_scores.append(scores)
        mlvl_bboxes = torch.cat(mlvl_bboxes)
        if rescale:
            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
        mlvl_scores = torch.cat(mlvl_scores)
        if self.use_sigmoid_cls:
            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
            mlvl_scores = torch.cat([padding, mlvl_scores], dim=1)
        if nms:
            det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
                                                    cfg.score_thr, cfg.nms,
                                                    cfg.max_per_img)
            return det_bboxes, det_labels
        else:
            return mlvl_bboxes, mlvl_scores