code: def prepare_for_coco_keypoint(self, predictions):
      coco_results = []
      for original_id, prediction in predictions.items():
          if len(prediction) == 0:
              continue
  
          boxes = prediction["boxes"]
          boxes = convert_to_xywh(boxes).tolist()
          scores = prediction["scores"].tolist()
          labels = prediction["labels"].tolist()
          keypoints = prediction["keypoints"]
          keypoints = keypoints.flatten(start_dim=1).tolist()
  
          coco_results.extend(
              [
                  {
                      "image_id": original_id,
                      "category_id": labels[k],
                      "keypoints": keypoint,
                      "score": scores[k],
                  }
                  for k, keypoint in enumerate(keypoints)
              ]
          )
      return coco_results
  
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 9 for calling the CocoEvaluator obj's underlying member method code with example usage: obj.prepare_for_coco_keypoint(predictions) and returns: coco_results