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.
docstr: Get ignore masks to mask out losses.
docstr_tok: ["Get","ignore","masks","to","mask","out","losses","."]
code: def _get_mask(self, anno, idx):
      """Get ignore masks to mask out losses."""
      coco = self.coco
      img_info = coco.loadImgs(self.img_ids[idx])[0]
  
      m = np.zeros(
          (img_info["height"], img_info["width"]), dtype=np.float32
      )
  
      for obj in anno:
          if "segmentation" in obj:
              if obj["iscrowd"]:
                  rle = pycocotools.mask.frPyObjects(
                      obj["segmentation"],
                      img_info["height"],
                      img_info["width"],
                  )
                  m += pycocotools.mask.decode(rle)
              elif obj["num_keypoints"] == 0:
                  rles = pycocotools.mask.frPyObjects(
                      obj["segmentation"],
                      img_info["height"],
                      img_info["width"],
                  )
                  for rle in rles:
                      m += pycocotools.mask.decode(rle)
  
      return m < 0.5
  
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 9 for calling the KeypointBottomUpCocoDataset obj's underlying member method code with example usage: obj._get_mask(anno, idx) and returns: unknown