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 __init__(
      self,
      anno_file,
      num_samples,
      num_joints,
      output_eval,
      iou_type="keypoints",
      in_vis_thre=0.2,
      oks_thre=0.9,
      save_prediction_only=False,
  ):
      super(KeyPointTopDownCOCOEval, self).__init__()
      self.coco = COCO(anno_file)
      self.num_samples = num_samples
      self.num_joints = num_joints
      self.iou_type = iou_type
      self.in_vis_thre = in_vis_thre
      self.oks_thre = oks_thre
      self.output_eval = output_eval
      self.res_file = os.path.join(
          output_eval, "keypoints_results.json"
      )
      self.save_prediction_only = save_prediction_only
      self.reset()
  
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 1 to create a new KeyPointTopDownCOCOEval object from inherited base classes: object with example: obj = KeyPointTopDownCOCOEval(anno_file, num_samples, num_joints, output_eval, iou_type, in_vis_thre, oks_thre, save_prediction_only)