code: def evaluate(model, data_loader, device):
      # Lorenzo: splitted evaluation in "evaluate_coco" and "evaluate_lvis"
      n_threads = torch.get_num_threads()
      # FIXME remove this and make paste_masks_in_image run on the GPU
      torch.set_num_threads(1)
      cpu_device = torch.device("cpu")
      model.eval()
      metric_logger = MetricLogger(delimiter="  ")
  
      det_api = get_detection_api_from_dataset(data_loader.dataset)
      iou_types = _get_iou_types(model)
      if isinstance(det_api, COCO):
          result = evaluate_coco(
              model,
              data_loader,
              device,
              det_api,
              metric_logger,
              cpu_device,
              iou_types,
          )
      else:
          result = evaluate_lvis(
              model,
              data_loader,
              device,
              det_api,
              metric_logger,
              cpu_device,
              iou_types,
          )
  
      torch.set_num_threads(n_threads)
      return result
  
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 5 for calling a global function with example usage: evaluate(model, data_loader, device) and returns: result