code: def test_multi_scale_roi_align(self):
      class TransformModule(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.model = ops.MultiScaleRoIAlign(
                  ["feat1", "feat2"], 3, 2
              )
              self.image_sizes = [(512, 512)]
  
          def forward(self, input, boxes):
              return self.model(input, boxes, self.image_sizes)
  
      i = OrderedDict()
      i["feat1"] = torch.rand(1, 5, 64, 64)
      i["feat2"] = torch.rand(1, 5, 16, 16)
      boxes = torch.rand(6, 4) * 256
      boxes[:, 2:] += boxes[:, :2]
  
      i1 = OrderedDict()
      i1["feat1"] = torch.rand(1, 5, 64, 64)
      i1["feat2"] = torch.rand(1, 5, 16, 16)
      boxes1 = torch.rand(6, 4) * 256
      boxes1[:, 2:] += boxes1[:, :2]
  
      self.run_model(
          TransformModule(),
          [
              (
                  i,
                  [boxes],
              ),
              (
                  i1,
                  [boxes1],
              ),
          ],
      )
  
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 18 for calling the TestONNXExporter obj's underlying member method code with example usage: obj.test_multi_scale_roi_align() and returns: self