code: def _init_test_rpn():
      anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
      aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
      rpn_anchor_generator = rpn.AnchorGenerator(
          anchor_sizes, aspect_ratios
      )
      out_channels = 256
      rpn_head = rpn.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
  
      return 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,
      )
  
imports: import os
  import unittest
  from collections import OrderedDict
  from typing import List, Mapping, Tuple
  import onnx_test_common
  import parameterized
  import PIL
  import pytorch_test_common
  import test_models
  import torch
  import torchvision
  from pytorch_test_common import skipIfUnsupportedMinOpsetVersion, skipScriptTest
  from torch import nn
  from torch.testing._internal import common_utils
  from torchvision import ops
  from torchvision.models.detection import faster_rcnn, image_list, keypoint_rcnn, mask_rcnn, roi_heads, rpn, transform
label_desc: Use image node_id 6 for calling a global function with example usage: _init_test_rpn() and returns: rpn