code: def _get_iou_types(model):
      model_without_ddp = model
      if isinstance(model, torch.nn.parallel.DistributedDataParallel):
          model_without_ddp = model.module
      iou_types = ["bbox"]
      if isinstance(
          model_without_ddp, torchvision.models.detection.MaskRCNN
      ):
          iou_types.append("segm")
      if isinstance(
          model_without_ddp, torchvision.models.detection.KeypointRCNN
      ):
          iou_types.append("keypoints")
      return iou_types
  
file_path: avalanche/examples/tvdetection/engine.py
imports: import math
  import sys
  import time
  import torch
  import torchvision.models.detection.mask_rcnn
  from lvis import LVIS
  from pycocotools.coco import COCO
  from torch.utils.data import Subset
  from avalanche.benchmarks.utils import _make_taskaware_classification_dataset, _taskaware_classification_subset, _concat_taskaware_classification_datasets
  from examples.tvdetection.coco_eval import CocoEvaluator
  from examples.tvdetection.coco_utils import CocoDetection, convert_to_coco_api
  from examples.tvdetection.lvis_eval import LvisEvaluator
  from examples.tvdetection.utils import MetricLogger, SmoothedValue, reduce_dict
label_desc: Use image node_id 2 for calling a global function with example usage: _get_iou_types(model) and returns: iou_types