def postprocess(self, data: Dict[(str, torch.Tensor)], height: int, width: int) -> torch.Tensor:
    (loc, conf, iou) = (data['loc'], data['conf'], data['iou'])
    scale = torch.tensor([width, height, width, height, width, height, width, height, width, height, width, height, width, height], device=loc.device, dtype=loc.dtype)
    priors = _PriorBox(self.min_sizes, self.steps, self.clip, image_size=(height, width))
    priors = priors.to(loc.device, loc.dtype)
    boxes = _decode(loc, priors(), self.variance)
    boxes = (boxes * scale)
    (cls_scores, iou_scores) = (conf[:, 1], iou[:, 0])
    scores = (cls_scores * iou_scores.clamp(0.0, 1.0)).sqrt()
    inds = (scores > self.confidence_threshold)
    (boxes, scores) = (boxes[inds], scores[inds])
    order = scores.sort(descending=True)[1][:self.top_k]
    (boxes, scores) = (boxes[order], scores[order])
    dets = torch.cat((boxes, scores[:, None]), dim=(- 1))
    keep = self.nms(boxes[:, :4], scores, self.nms_threshold)
    if (len(keep) > 0):
        dets = dets[keep, :]
    return dets[:self.keep_top_k]
def postprocess(self, data: Dict[(str, torch.Tensor)], height: int, width: int) -> torch.Tensor:
    (loc, conf, iou) = (data['loc'], data['conf'], data['iou'])
    scale = torch.tensor([width, height, width, height, width, height, width, height, width, height, width, height, width, height], device=loc.device, dtype=loc.dtype)
    priors = _PriorBox(self.min_sizes, self.steps, self.clip, image_size=(height, width))
    priors = priors.to(loc.device, loc.dtype)
    boxes = _decode(loc, priors(), self.variance)
    boxes = (boxes * scale)
    (cls_scores, iou_scores) = (conf[:, 1], iou[:, 0])
    scores = (cls_scores * iou_scores.clamp(0.0, 1.0)).sqrt()
    inds = (scores > self.confidence_threshold)
    (boxes, scores) = (boxes[inds], scores[inds])
    order = scores.sort(descending=True)[1][:self.top_k]
    (boxes, scores) = (boxes[order], scores[order])
    dets = torch.cat((boxes, scores[:, None]), dim=(- 1))
    keep = self.nms(boxes[:, :4], scores, self.nms_threshold)
    if (len(keep) > 0):
        dets = dets[keep, :]
    return dets[:self.keep_top_k]