code: def prepare_for_coco_segmentation(self, predictions):
      coco_results = []
      for original_id, prediction in predictions.items():
          if len(prediction) == 0:
              continue
  
          scores = prediction["scores"]
          labels = prediction["labels"]
          masks = prediction["masks"]
  
          masks = masks > 0.5
  
          scores = prediction["scores"].tolist()
          labels = prediction["labels"].tolist()
  
          rles = [
              mask_util.encode(
                  np.array(
                      mask[0, :, :, np.newaxis],
                      dtype=np.uint8,
                      order="F",
                  )
              )[0]
              for mask in masks
          ]
          for rle in rles:
              rle["counts"] = rle["counts"].decode("utf-8")
  
          coco_results.extend(
              [
                  {
                      "image_id": original_id,
                      "category_id": labels[k],
                      "segmentation": rle,
                      "score": scores[k],
                  }
                  for k, rle in enumerate(rles)
              ]
          )
      return coco_results
  
imports: import json
  from collections import defaultdict
  import numpy
  import copy
  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 8 for calling the CocoEvaluator obj's underlying member method code with example usage: obj.prepare_for_coco_segmentation(predictions) and returns: coco_results