class_docstr: COCO dataset for bottom-up 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.
      anno_path (str): Relative path to the annotation file.
      image_dir (str): Path to a directory where images are held.
          Default: None.
      num_joints (int): keypoint numbers
      transform (composed(operators)): A sequence of data transforms.
      shard (list): [rank, worldsize], the distributed env params
      test_mode (bool): Store True when building test or
          validation dataset. Default: False.
code: def _get_labels(self, anno, idx):
      num_people = len(anno)
      gt_labels = np.zeros((num_people, 1), dtype=np.float32)
  
      for idx, obj in enumerate(anno):
          if "category_id" in obj:
              catid = obj["category_id"]
              gt_labels[idx, 0] = self.catid2clsid[catid]
      return gt_labels
  
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 7 for calling the KeypointBottomUpCocoDataset obj's underlying member method code with example usage: obj._get_labels(anno, idx) and returns: gt_labels