code: def nms(
      boxes: Tensor, scores: Tensor, iou_threshold: float
  ) -> Tensor:
      """
      Performs non-maximum suppression (NMS) on the boxes according
      to their intersection-over-union (IoU).
  
      NMS iteratively removes lower scoring boxes which have an
      IoU greater than ``iou_threshold`` with another (higher scoring)
      box.
  
      If multiple boxes have the exact same score and satisfy the IoU
      criterion with respect to a reference box, the selected box is
      not guaranteed to be the same between CPU and GPU. This is similar
      to the behavior of argsort in PyTorch when repeated values are present.
  
      Args:
          boxes (Tensor[N, 4])): boxes to perform NMS on. They
              are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and
              ``0 <= y1 < y2``.
          scores (Tensor[N]): scores for each one of the boxes
          iou_threshold (float): discards all overlapping boxes with IoU > iou_threshold
  
      Returns:
          Tensor: int64 tensor with the indices of the elements that have been kept
          by NMS, sorted in decreasing order of scores
      """
      if not torch.jit.is_scripting() and not torch.jit.is_tracing():
          _log_api_usage_once(nms)
      _assert_has_ops()
      return torch.ops.torchvision.nms(boxes, scores, iou_threshold)
  
imports: from typing import Tuple
  import torch
  import torchvision
  from torch import Tensor
  from torchvision.extension import _assert_has_ops
  from ..utils import _log_api_usage_once
  from ._box_convert import _box_cxcywh_to_xyxy, _box_xywh_to_xyxy, _box_xyxy_to_cxcywh, _box_xyxy_to_xywh
  from ._utils import _upcast
label_desc: Use image node_id 1 for calling a global function with example usage: nms(boxes, scores, iou_threshold) and returns: torch