code: def run_model(
      self,
      model,
      inputs_list,
      do_constant_folding=True,
      dynamic_axes=None,
      output_names=None,
      input_names=None,
      opset_version: Optional[int] = None,
  ):
      if opset_version is None:
          opset_version = _register_onnx_ops.BASE_ONNX_OPSET_VERSION
  
      model.eval()
  
      onnx_io = io.BytesIO()
      if isinstance(inputs_list[0][-1], dict):
          torch_onnx_input = inputs_list[0] + ({},)
      else:
          torch_onnx_input = inputs_list[0]
      # export to onnx with the first input
      torch.onnx.export(
          model,
          torch_onnx_input,
          onnx_io,
          do_constant_folding=do_constant_folding,
          opset_version=opset_version,
          dynamic_axes=dynamic_axes,
          input_names=input_names,
          output_names=output_names,
          verbose=True,
      )
      # validate the exported model with onnx runtime
      for test_inputs in inputs_list:
          with torch.no_grad():
              if isinstance(test_inputs, torch.Tensor) or isinstance(
                  test_inputs, list
              ):
                  test_inputs = (test_inputs,)
              test_ouputs = model(*test_inputs)
              if isinstance(test_ouputs, torch.Tensor):
                  test_ouputs = (test_ouputs,)
          self.ort_validate(onnx_io, test_inputs, test_ouputs)
  
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 2 for calling the TestONNXExporter obj's underlying member method code with example usage: obj.run_model(model, inputs_list, do_constant_folding, dynamic_axes, output_names, input_names, opset_version) without return types