code: def test_roi_align(self):
      x = torch.rand(1, 1, 10, 10, dtype=torch.float32)
      single_roi = torch.tensor([[0, 0, 0, 4, 4]], dtype=torch.float32)
      model = ops.RoIAlign((5, 5), 1, 2)
      self.run_model(model, [(x, single_roi)])
  
      x = torch.rand(1, 1, 10, 10, dtype=torch.float32)
      single_roi = torch.tensor([[0, 0, 0, 4, 4]], dtype=torch.float32)
      model = ops.RoIAlign((5, 5), 1, -1)
      self.run_model(model, [(x, single_roi)])
  
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 7 for calling the TestONNXExporter obj's underlying member method code with example usage: obj.test_roi_align() without return types