code: def test_keypoint_rcnn(self):
      images, test_images = self.get_test_images()
      dummy_images = [torch.ones(3, 100, 100) * 0.3]
      model = models.detection.keypoint_rcnn.keypointrcnn_resnet50_fpn(
          weights=models.detection.keypoint_rcnn.KeypointRCNN_ResNet50_FPN_Weights.DEFAULT,
          min_size=200,
          max_size=300,
      )
      model.eval()
      model(images)
      self.run_model(
          model,
          [(images,), (test_images,), (dummy_images,)],
          input_names=["images_tensors"],
          output_names=["outputs1", "outputs2", "outputs3", "outputs4"],
          dynamic_axes={"images_tensors": [0, 1, 2]},
      )
  
      self.run_model(
          model,
          [(dummy_images,), (test_images,)],
          input_names=["images_tensors"],
          output_names=["outputs1", "outputs2", "outputs3", "outputs4"],
          dynamic_axes={"images_tensors": [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 26 for calling the TestONNXExporter obj's underlying member method code with example usage: obj.test_keypoint_rcnn() without return types