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 _coco_keypoint_results_one_category_kernel(self, data_pack):
      cat_id = data_pack["cat_id"]
      keypoints = data_pack["keypoints"]
      cat_results = []
  
      for img_kpts in keypoints:
          if len(img_kpts) == 0:
              continue
  
          _key_points = np.array(
              [img_kpts[k]["keypoints"] for k in range(len(img_kpts))]
          )
          _key_points = _key_points.reshape(_key_points.shape[0], -1)
  
          result = [
              {
                  "image_id": img_kpts[k]["image"],
                  "category_id": cat_id,
                  "keypoints": _key_points[k].tolist(),
                  "score": img_kpts[k]["score"],
                  "center": list(img_kpts[k]["center"]),
                  "scale": list(img_kpts[k]["scale"]),
              }
              for k in range(len(img_kpts))
          ]
          cat_results.extend(result)
  
      return cat_results
  
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 5 for calling the KeyPointTopDownCOCOEval obj's underlying member method code with example usage: obj._coco_keypoint_results_one_category_kernel(data_pack) and returns: cat_results