code: def test_resize_images(self):
      class TransformModule(torch.nn.Module):
          def __init__(self_module):
              super().__init__()
              self_module.transform = (
                  self._init_test_generalized_rcnn_transform()
              )
  
          def forward(self_module, images):
              return self_module.transform.resize(images, None)[0]
  
      input = torch.rand(3, 10, 20)
      input_test = torch.rand(3, 100, 150)
      self.run_model(
          TransformModule(),
          [(input,), (input_test,)],
          input_names=["input1"],
          dynamic_axes={"input1": [0, 1, 2]},
      )
  
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 11 for calling the TestONNXExporter obj's underlying member method code with example usage: obj.test_resize_images() and returns: self_module