code: def evaluate(self):
      # write predict results into json file
      json_str = json.dumps(self.results, indent=4)
      with open(self.results_file_name, "w") as json_file:
          json_file.write(json_str)
  
      # accumulate predictions from all images
      coco_true = self.coco
      coco_pre = coco_true.loadRes(self.results_file_name)
  
      self.coco_evaluator = COCOeval(
          cocoGt=coco_true, cocoDt=coco_pre, iouType=self.iou_type
      )
  
      self.coco_evaluator.evaluate()
      self.coco_evaluator.accumulate()
      print(f"IoU metric: {self.iou_type}")
      self.coco_evaluator.summarize()
  
      coco_info = self.coco_evaluator.stats.tolist()  # numpy to list
      return coco_info
  
imports: import os
  import time
  import json
  import copy
  import cv2
  import numpy
  import torch
  from torchmetrics.detection.mean_ap import MeanAveragePrecision
  import torchvision
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  from compression.api import DataLoader, Metric
label_desc: Use image node_id 4 for calling the EvalCOCOMetric obj's underlying member method code with example usage: obj.evaluate() and returns: coco_info