code: def _init_test_rpn(self):
      anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
      aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
      rpn_anchor_generator = AnchorGenerator(
          anchor_sizes, aspect_ratios
      )
      out_channels = 256
      rpn_head = RPNHead(
          out_channels,
          rpn_anchor_generator.num_anchors_per_location()[0],
      )
      rpn_fg_iou_thresh = 0.7
      rpn_bg_iou_thresh = 0.3
      rpn_batch_size_per_image = 256
      rpn_positive_fraction = 0.5
      rpn_pre_nms_top_n = dict(training=2000, testing=1000)
      rpn_post_nms_top_n = dict(training=2000, testing=1000)
      rpn_nms_thresh = 0.7
      rpn_score_thresh = 0.0
  
      rpn = RegionProposalNetwork(
          rpn_anchor_generator,
          rpn_head,
          rpn_fg_iou_thresh,
          rpn_bg_iou_thresh,
          rpn_batch_size_per_image,
          rpn_positive_fraction,
          rpn_pre_nms_top_n,
          rpn_post_nms_top_n,
          rpn_nms_thresh,
          score_thresh=rpn_score_thresh,
      )
      return rpn
  
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 14 for calling the TestONNXExporter obj's underlying member method code with example usage: obj._init_test_rpn() and returns: rpn