class_docstr: MPII dataset for topdown pose estimation.
  
  The dataset loads raw features and apply specified transforms
  to return a dict containing the image tensors and other information.
  
  MPII keypoint indexes::
  
      0: 'right_ankle',
      1: 'right_knee',
      2: 'right_hip',
      3: 'left_hip',
      4: 'left_knee',
      5: 'left_ankle',
      6: 'pelvis',
      7: 'thorax',
      8: 'upper_neck',
      9: 'head_top',
      10: 'right_wrist',
      11: 'right_elbow',
      12: 'right_shoulder',
      13: 'left_shoulder',
      14: 'left_elbow',
      15: 'left_wrist',
  
  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.
code: def __init__(
      self, dataset_dir, image_dir, anno_path, num_joints, transform=[]
  ):
      super().__init__(
          dataset_dir, image_dir, anno_path, num_joints, transform
      )
  
      self.dataset_name = "mpii"
  
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 1 to create a new KeypointTopDownMPIIDataset object from inherited base classes: KeypointTopDownBaseDataset with example: obj = KeypointTopDownMPIIDataset(dataset_dir, image_dir, anno_path, num_joints, transform)