code: def _init_test_roi_heads_faster_rcnn(self):
      out_channels = 256
      num_classes = 91
  
      box_fg_iou_thresh = 0.5
      box_bg_iou_thresh = 0.5
      box_batch_size_per_image = 512
      box_positive_fraction = 0.25
      bbox_reg_weights = None
      box_score_thresh = 0.05
      box_nms_thresh = 0.5
      box_detections_per_img = 100
  
      box_roi_pool = ops.MultiScaleRoIAlign(
          featmap_names=["0", "1", "2", "3"],
          output_size=7,
          sampling_ratio=2,
      )
  
      resolution = box_roi_pool.output_size[0]
      representation_size = 1024
      box_head = TwoMLPHead(
          out_channels * resolution**2, representation_size
      )
  
      representation_size = 1024
      box_predictor = FastRCNNPredictor(
          representation_size, num_classes
      )
  
      roi_heads = RoIHeads(
          box_roi_pool,
          box_head,
          box_predictor,
          box_fg_iou_thresh,
          box_bg_iou_thresh,
          box_batch_size_per_image,
          box_positive_fraction,
          bbox_reg_weights,
          box_score_thresh,
          box_nms_thresh,
          box_detections_per_img,
      )
      return roi_heads
  
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 15 for calling the TestONNXExporter obj's underlying member method code with example usage: obj._init_test_roi_heads_faster_rcnn() and returns: roi_heads