code: def symbolic_multi_label_nms(g, boxes, scores, iou_threshold):
      boxes = opset11.unsqueeze(g, boxes, 0)
      scores = opset11.unsqueeze(g, opset11.unsqueeze(g, scores, 0), 0)
      max_output_per_class = g.op(
          "Constant",
          value_t=torch.tensor([sys.maxsize], dtype=torch.long),
      )
      iou_threshold = g.op(
          "Constant",
          value_t=torch.tensor([iou_threshold], dtype=torch.float),
      )
  
      # Cast boxes and scores to float32 in case they are float64 inputs
      nms_out = g.op(
          "NonMaxSuppression",
          g.op(
              "Cast", boxes, to_i=torch.onnx.TensorProtoDataType.FLOAT
          ),
          g.op(
              "Cast", scores, to_i=torch.onnx.TensorProtoDataType.FLOAT
          ),
          max_output_per_class,
          iou_threshold,
      )
      return opset11.squeeze(
          g,
          opset11.select(
              g,
              nms_out,
              1,
              g.op(
                  "Constant",
                  value_t=torch.tensor([2], dtype=torch.long),
              ),
          ),
          1,
      )
  
file_path: vision/torchvision/ops/_register_onnx_ops.py
imports: import sys
  import warnings
  import torch
  from torch.onnx import symbolic_opset11
  from torch.onnx.symbolic_helper import parse_args
label_desc: Use image node_id 1 for calling a global function with example usage: symbolic_multi_label_nms(g, boxes, scores, iou_threshold) and returns: opset11