code: def get_features(self, images):
      s0, s1 = images.shape[-2:]
      features = [
          ("0", torch.rand(2, 256, s0 // 4, s1 // 4)),
          ("1", torch.rand(2, 256, s0 // 8, s1 // 8)),
          ("2", torch.rand(2, 256, s0 // 16, s1 // 16)),
          ("3", torch.rand(2, 256, s0 // 32, s1 // 32)),
          ("4", torch.rand(2, 256, s0 // 64, s1 // 64)),
      ]
      features = OrderedDict(features)
      return features
  
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 16 for calling the TestONNXExporter obj's underlying member method code with example usage: obj.get_features(images) and returns: features