class_docstr: refer to
  https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
  Copyright (c) Microsoft, under the MIT License.
class_docstr_tok: ["refer","to","https",":","\/\/github.com\/leoxiaobin\/deep-high-resolution-net.pytorch","Copyright","(","c",")","Microsoft",",","under","the","MIT","License","."]
code: def _write_coco_keypoint_results(self, keypoints):
      data_pack = [
          {
              "cat_id": 1,
              "cls": "person",
              "ann_type": "keypoints",
              "keypoints": keypoints,
          }
      ]
      results = self._coco_keypoint_results_one_category_kernel(
          data_pack[0]
      )
      if not os.path.exists(self.output_eval):
          os.makedirs(self.output_eval)
      with open(self.res_file, "w") as f:
          json.dump(results, f, sort_keys=True, indent=4)
          logger.info(
              f"The keypoint result is saved to {self.res_file}."
          )
      try:
          json.load(open(self.res_file))
      except Exception:
          content = []
          with open(self.res_file, "r") as f:
              for line in f:
                  content.append(line)
          content[-1] = "]"
          with open(self.res_file, "w") as f:
              for c in content:
                  f.write(c)
  
imports: import os
  import json
  from collections import defaultdict, OrderedDict
  import numpy
  import paddle
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  from ..modeling.keypoint_utils import oks_nms, keypoint_pck_accuracy, keypoint_auc, keypoint_epe
  from scipy.io import loadmat, savemat
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 4 for calling the KeyPointTopDownCOCOEval obj's underlying member method code with example usage: obj._write_coco_keypoint_results(keypoints) without return types