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 accumulate(self):
      self.get_final_results(
          self.results["all_preds"],
          self.results["all_boxes"],
          self.results["image_path"],
      )
      if self.save_prediction_only:
          logger.info(
              f"The keypoint result is saved to {self.res_file} "
              "and do not evaluate the mAP."
          )
          return
      coco_dt = self.coco.loadRes(self.res_file)
      coco_eval = COCOeval(self.coco, coco_dt, "keypoints")
      coco_eval.params.useSegm = None
      coco_eval.evaluate()
      coco_eval.accumulate()
      coco_eval.summarize()
  
      keypoint_stats = []
      for ind in range(len(coco_eval.stats)):
          keypoint_stats.append((coco_eval.stats[ind]))
      self.eval_results["keypoint"] = keypoint_stats
  
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 7 for calling the KeyPointTopDownCOCOEval obj's underlying member method code with example usage: obj.accumulate() without return types