code: def ort_validate(self, onnx_io, inputs, outputs):
      inputs, _ = torch.jit._flatten(inputs)
      outputs, _ = torch.jit._flatten(outputs)
  
      def to_numpy(tensor):
          if tensor.requires_grad:
              return tensor.detach().cpu().numpy()
          else:
              return tensor.cpu().numpy()
  
      inputs = list(map(to_numpy, inputs))
      outputs = list(map(to_numpy, outputs))
  
      ort_session = onnxruntime.InferenceSession(
          onnx_io.getvalue(),
          providers=onnxruntime.get_available_providers(),
      )
      # compute onnxruntime output prediction
      ort_inputs = {
          ort_session.get_inputs()[i].name: inpt
          for i, inpt in enumerate(inputs)
      }
      ort_outs = ort_session.run(None, ort_inputs)
  
      for i in range(0, len(outputs)):
          torch.testing.assert_close(
              outputs[i], ort_outs[i], rtol=1e-03, atol=1e-05
          )
  
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 3 for calling the TestONNXExporter obj's underlying member method code with example usage: obj.ort_validate(onnx_io, inputs, outputs) and returns: tensor, tensor