code: def test_clip_boxes_to_image(self):
      boxes = torch.randn(5, 4) * 500
      boxes[:, 2:] += boxes[:, :2]
      size = torch.randn(200, 300)
  
      size_2 = torch.randn(300, 400)
  
      class Module(torch.nn.Module):
          def forward(self, boxes, size):
              return ops.boxes.clip_boxes_to_image(boxes, size.shape)
  
      self.run_model(
          Module(),
          [(boxes, size), (boxes, size_2)],
          input_names=["boxes", "size"],
          dynamic_axes={"size": [0, 1]},
      )
  
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 6 for calling the TestONNXExporter obj's underlying member method code with example usage: obj.test_clip_boxes_to_image() and returns: ops