code: def test_nms(self):
      num_boxes = 100
      boxes = torch.rand(num_boxes, 4)
      boxes[:, 2:] += boxes[:, :2]
      scores = torch.randn(num_boxes)
  
      class Module(torch.nn.Module):
          def forward(self, boxes, scores):
              return ops.nms(boxes, scores, 0.5)
  
      self.run_model(Module(), [(boxes, scores)])
  
imports: import io
  from collections import OrderedDict
  from typing import List, Optional, Tuple
  import pytest
  import torch
  from common_utils import assert_equal, set_rng_seed
  from torchvision import models, ops
  from torchvision.models.detection.faster_rcnn import FastRCNNPredictor, TwoMLPHead
  from torchvision.models.detection.image_list import ImageList
  from torchvision.models.detection.roi_heads import RoIHeads
  from torchvision.models.detection.rpn import AnchorGenerator, RegionProposalNetwork, RPNHead
  from torchvision.models.detection.transform import GeneralizedRCNNTransform
  from torchvision.ops import _register_onnx_ops
label_desc: Use image node_id 4 for calling the TestONNXExporter obj's underlying member method code with example usage: obj.test_nms() and returns: ops