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: Return pycocotools object for evaluation purposes.
docstr_tok: ["Return","pycocotools","object","for","evaluation","purposes","."]
code: def coco(self):
      """Return pycocotools object for evaluation purposes."""
      if not self._coco:
          raise ValueError(
              "No coco objects found, dataset not initialized."
          )
      if len(self._coco) > 1:
          raise NotImplementedError(
              "Currently we don't support evaluating {} JSON files".format(
                  len(self._coco)
              )
          )
      return self._coco[0]
  
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 6 for calling the COCOKeyPoints obj's underlying member method code with example usage: obj.coco() and returns: self