code: def test_shufflenet_v2_dynamic_axes(self):
      model = models.shufflenet_v2_x0_5(
          weights=models.ShuffleNet_V2_X0_5_Weights.DEFAULT
      )
      dummy_input = torch.randn(1, 3, 224, 224, requires_grad=True)
      test_inputs = torch.cat(
          [dummy_input, dummy_input, dummy_input], 0
      )
  
      self.run_model(
          model,
          [(dummy_input,), (test_inputs,)],
          input_names=["input_images"],
          output_names=["output"],
          dynamic_axes={
              "input_images": {0: "batch_size"},
              "output": {0: "batch_size"},
          },
      )
  
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 27 for calling the TestONNXExporter obj's underlying member method code with example usage: obj.test_shufflenet_v2_dynamic_axes() without return types