class_docstr: COCO keypoint detection dataset.
  
  Parameters
  ----------
  root : str, default '~/mxnet/datasets/coco'
      Path to folder storing the dataset.
  splits : list of str, default ['person_keypoints_val2017']
      Json annotations name.
      Candidates can be: person_keypoints_val2017, person_keypoints_train2017.
  check_centers : bool, default is False
      If true, will force check centers of bbox and keypoints, respectively.
      If centers are far away from each other, remove this label.
  skip_empty : bool, default is False
      Whether skip entire image if no valid label is found. Use `False` if this dataset is
      for validation to avoid COCO metric error.
docstr: Get geometric center of all keypoints
docstr_tok: ["Get","geometric","center","of","all","keypoints"]
code: def _get_keypoints_center_count(self, keypoints):
      """Get geometric center of all keypoints"""
      keypoint_x = np.sum(keypoints[:, 0, 0] * (keypoints[:, 0, 1] > 0))
      keypoint_y = np.sum(keypoints[:, 1, 0] * (keypoints[:, 1, 1] > 0))
      num = float(np.sum(keypoints[:, 0, 1]))
      return np.array([keypoint_x / num, keypoint_y / num]), num
  
imports: from __future__ import absolute_import
  from __future__ import division
  import os
  import copy
  import numpy
  import mxnet
  from .utils import try_import_pycocotools
  from ..base import VisionDataset
  from ...utils.bbox import bbox_xywh_to_xyxy, bbox_clip_xyxy
label_desc: Use image node_id 12 for calling the COCOKeyPoints obj's underlying member method code with example usage: obj._get_keypoints_center_count(keypoints) and returns: np,  num