class_docstr: 24 keypoints 3D dataset for pose estimation. 
  
  each item is a list of images
  
  The dataset loads raw features and apply specified transforms
  to return a dict containing the image tensors and other information.
  
  Args:
      dataset_dir (str): Root path to the dataset.
      image_dir (str): Path to a directory where images are held.
class_docstr_tok: ["24","keypoints","3D","dataset","for","pose","estimation",".","each","item","is","a","list","of","images","The","dataset","loads","raw","features","and","apply","specified","transforms","to","return","a","dict","containing","the","image","tensors","and","other","information",".","Args",":","dataset_dir","(","str",")",":","Root","path","to","the","dataset",".","image_dir","(","str",")",":","Path","to","a","directory","where","images","are","held","."]
code: def parse_dataset(
      self,
  ):
      return
  
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
  from paddle.io import Dataset
label_desc: Use image node_id 9 for calling the Keypoint3DMultiFramesDataset obj's underlying member method code with example usage: obj.parse_dataset() without return types