code: def __getitem__(self, index):
      seq_len = self.sequence_lens[index]
      index_in_seq = self.indices_in_seq[index]
      kps_3d = self.kp3ds[index]
      rot = self.rotation[index]
      if not self.is_train:
          kps_2d = self.kp2ds[index]
          # don't use
          diff1 = diff2 = self.kp2ds[index]
      else:
          kps_2d = self.kp2ds[index]
          if self.frame_interval + index < seq_len:
              diff1_index = index + self.frame_interval
          else:
              diff1_index = index - self.frame_interval
          diff1 = self.kp2ds[diff1_index]
  
          diff_dist = np.random.randint(
              -index_in_seq, seq_len - index_in_seq
          )
          while abs(diff_dist) < self.min_diff_dist:
              diff_dist = np.random.randint(
                  -index_in_seq, seq_len - index_in_seq
              )
          diff2_index = index + diff_dist
          diff2 = self.kp2ds[diff2_index]
  
      rot = self.rotation[index]
      # for valdiation, simply ignore scale
      if self.scales is None or not self.is_train:
          scale = 0
      else:
          scale = self.scales[index]
      return kps_2d, kps_3d, rot, diff1, diff2, scale
  
imports: import sys
  from torch.utils.data import Dataset, DataLoader
  import os
  import os.path
  import glob
  import numpy
  import random
  import cv2
  import pickle
  import json
  import h5py
  import torch
  import matplotlib.pyplot
  from lib.utils.misc import process_dataset_for_video
label_desc: Use image node_id 5 for calling the MPIINFDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: kps_2d,  kps_3d,  rot,  diff1,  diff2,  scale