docstr: Keypoint evaluation.
  
  Args:
      res_file (str): Json file stored prediction results.
      metrics (str | list[str]): Metric to be performed.
          Options: 'PCK', 'AUC', 'EPE'.
      pck_thr (float): PCK threshold, default as 0.2.
      auc_nor (float): AUC normalization factor, default as 30 pixel.
  
  Returns:
      List: Evaluation results for evaluation metric.
code: def evaluate(self, res_file, metrics, pck_thr=0.2, auc_nor=30):
      """Keypoint evaluation.
  
      Args:
          res_file (str): Json file stored prediction results.
          metrics (str | list[str]): Metric to be performed.
              Options: 'PCK', 'AUC', 'EPE'.
          pck_thr (float): PCK threshold, default as 0.2.
          auc_nor (float): AUC normalization factor, default as 30 pixel.
  
      Returns:
          List: Evaluation results for evaluation metric.
      """
      info_str = []
  
      with open(res_file, "r") as fin:
          preds = json.load(fin)
      assert len(preds) == len(self.db)
  
      outputs = []
      gts = []
      masks = []
      threshold_bbox = []
  
      for pred, item in zip(preds, self.db):
          outputs.append(np.array(pred["keypoints"])[:, :-1])
          gts.append(np.array(item["gt_joints"])[:, :-1])
          masks.append((np.array(item["joints_vis"])[:, 0]) > 0)
          if "PCK" in metrics:
              bbox = np.array(item["bbox"])
              bbox_thr = np.max(bbox[2:])
              threshold_bbox.append(np.array([bbox_thr, bbox_thr]))
  
      outputs = np.array(outputs)
      gts = np.array(gts)
      masks = np.array(masks)
      threshold_bbox = np.array(threshold_bbox)
  
      if "PCK" in metrics:
          _, pck, _ = keypoint_pck_accuracy(
              outputs, gts, masks, pck_thr, threshold_bbox
          )
          info_str.append(("PCK", pck))
  
      if "AUC" in metrics:
          info_str.append(
              ("AUC", keypoint_auc(outputs, gts, masks, auc_nor))
          )
  
      if "EPE" in metrics:
          info_str.append(("EPE", keypoint_epe(outputs, gts, masks)))
  
      name_value = OrderedDict(info_str)
  
      return name_value
  
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 10 for calling the KeyPointTopDownCOCOWholeBadyHandEval obj's underlying member method code with example usage: obj.evaluate(res_file, metrics, pck_thr, auc_nor) and returns: name_value