class_docstr: COCO dataset for top-down pose estimation. 
  
  The dataset loads raw features and apply specified transforms
  to return a dict containing the image tensors and other information.
  
  COCO keypoint indexes:
  
      0: 'nose',
      1: 'left_eye',
      2: 'right_eye',
      3: 'left_ear',
      4: 'right_ear',
      5: 'left_shoulder',
      6: 'right_shoulder',
      7: 'left_elbow',
      8: 'right_elbow',
      9: 'left_wrist',
      10: 'right_wrist',
      11: 'left_hip',
      12: 'right_hip',
      13: 'left_knee',
      14: 'right_knee',
      15: 'left_ankle',
      16: 'right_ankle'
  
  Args:
      dataset_dir (str): Root path to the dataset.
      image_dir (str): Path to a directory where images are held.
      anno_path (str): Relative path to the annotation file.
      num_joints (int): Keypoint numbers
      trainsize (list):[w, h] Image target size
      transform (composed(operators)): A sequence of data transforms.
      bbox_file (str): Path to a detection bbox file
          Default: None.
      use_gt_bbox (bool): Whether to use ground truth bbox
          Default: True.
      pixel_std (int): The pixel std of the scale
          Default: 200.
      image_thre (float): The threshold to filter the detection box
          Default: 0.0.
code: def parse_dataset(self):
      if self.use_gt_bbox:
          self.db = self._load_coco_keypoint_annotations()
      else:
          self.db = self._load_coco_person_detection_results()
  
imports: import os
  import cv2
  import numpy
  import json
  import copy
  import pycocotools
  from pycocotools.coco import COCO
  from .dataset import DetDataset
  from ppdet.core.workspace import register, serializable
label_desc: Use image node_id 2 for calling the KeypointTopDownCocoDataset obj's underlying member method code with example usage: obj.parse_dataset() without return types