code: def evaluate_predictions_on_coco(json_result_file, iou_type="bbox"):
      coco_results = []
      with open(json_result_file, "r") as f:
          for line in f:
              coco_results.append(json.loads(line))
  
      coco_gt = COCO(
          str(BASEDIR / "annotations/instances_val2017.json")
      )
      set_of_json = remove_dup(
          [json.dumps(d, cls=NpEncoder) for d in coco_results]
      )
      unique_list = [json.loads(s) for s in set_of_json]
  
      with open(f"{json_result_file}.flattend", "w") as f:
          json.dump(unique_list, f)
  
      coco_dt = coco_gt.loadRes(str(f"{json_result_file}.flattend"))
      coco_eval = COCOeval(coco_gt, coco_dt, iou_type)
      coco_eval.evaluate()
      coco_eval.accumulate()
      coco_eval.summarize()
      return coco_eval
  
imports: import json
  import pathlib
  import zipfile
  import numpy
  from tinygrad.helpers import fetch
  import pycocotools._mask
  from examples.mask_rcnn import Masker
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
label_desc: Use image node_id 10 for calling a global function with example usage: evaluate_predictions_on_coco(json_result_file, iou_type) and returns: coco_eval