code: def loadRes(self, resFile):
      """
      Load result file and return a result api object.
      :param   resFile (str)     : file name of result file
      :return: res (obj)         : result api object
      """
      res = COCO()
      res.dataset["images"] = [img for img in self.dataset["images"]]
  
      # print('Loading and preparing results...')
      # tic = time.time()
      if isinstance(resFile, torch._six.string_classes):
          anns = json.load(open(resFile))
      elif type(resFile) == np.ndarray:
          anns = self.loadNumpyAnnotations(resFile)
      else:
          anns = resFile
      assert type(anns) == list, "results in not an array of objects"
      annsImgIds = [ann["image_id"] for ann in anns]
      assert set(annsImgIds) == (
          set(annsImgIds) & set(self.getImgIds())
      ), "Results do not correspond to current coco set"
      if "caption" in anns[0]:
          imgIds = set(
              [img["id"] for img in res.dataset["images"]]
          ) & set([ann["image_id"] for ann in anns])
          res.dataset["images"] = [
              img
              for img in res.dataset["images"]
              if img["id"] in imgIds
          ]
          for id, ann in enumerate(anns):
              ann["id"] = id + 1
      elif "bbox" in anns[0] and not anns[0]["bbox"] == []:
          res.dataset["categories"] = copy.deepcopy(
              self.dataset["categories"]
          )
          for id, ann in enumerate(anns):
              bb = ann["bbox"]
              x1, x2, y1, y2 = [
                  bb[0],
                  bb[0] + bb[2],
                  bb[1],
                  bb[1] + bb[3],
              ]
              if "segmentation" not in ann:
                  ann["segmentation"] = [
                      [x1, y1, x1, y2, x2, y2, x2, y1]
                  ]
              ann["area"] = bb[2] * bb[3]
              ann["id"] = id + 1
              ann["iscrowd"] = 0
      elif "segmentation" in anns[0]:
          res.dataset["categories"] = copy.deepcopy(
              self.dataset["categories"]
          )
          for id, ann in enumerate(anns):
              # now only support compressed RLE format as segmentation results
              ann["area"] = maskUtils.area(ann["segmentation"])
              if "bbox" not in ann:
                  ann["bbox"] = maskUtils.toBbox(ann["segmentation"])
              ann["id"] = id + 1
              ann["iscrowd"] = 0
      elif "keypoints" in anns[0]:
          res.dataset["categories"] = copy.deepcopy(
              self.dataset["categories"]
          )
          for id, ann in enumerate(anns):
              s = ann["keypoints"]
              x = s[0::3]
              y = s[1::3]
              x1, x2, y1, y2 = (
                  np.min(x),
                  np.max(x),
                  np.min(y),
                  np.max(y),
              )
              ann["area"] = (x2 - x1) * (y2 - y1)
              ann["id"] = id + 1
              ann["bbox"] = [x1, y1, x2 - x1, y2 - y1]
      # print('DONE (t={:0.2f}s)'.format(time.time()- tic))
  
      res.dataset["annotations"] = anns
      createIndex(res)
      return res
  
imports: import json
  import copy
  from collections import defaultdict
  import numpy
  import torch
  import torch._six
  from pycocotools.cocoeval import COCOeval
  from pycocotools.coco import COCO
  import pycocotools.mask
  from train_utils.distributed_utils import all_gather
label_desc: Use image node_id 5 for calling a global function with example usage: loadRes(resFile) and returns: res