code: def __getitem__(self, index):
      if not self.seed_set:
          self.seed_set = True
          random.seed(index)
          np.random.seed(index)
  
      seq_len = self.sequence_lens[index]
      index_in_seq = self.indices_in_seq[index]
      kps_3d = self.kp3ds[index]
      rot = self.rotation[index]
      # index in its sequence
      kps_2d = self.kp2ds[index]
      kps_3d = self.kp3ds[index]
      diff1 = self.kp2ds[self.diff_indices[index]]
      if seq_len == 1:
          diff_dist = 0
      else:
          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]
      # current form: F * J * 2
      # we need to swap the last two axis, so that the item will be in the form J * 2 * F where
      # J is the number of keypoints and F is the number of frames
      # kps_2d = kps_2d.permute(1, 2, 0).contiguous()
      # diff = self.diff[all_indices].permute(1, 2, 0).contiguous()
      kps_2d = self.kp2ds[index]
  
      rot = self.rotation[index]
      # the flag will always be 1 when no extra data is used
      # flag = self.flags[index]
      # for valdiation, simply ignore scale
      scale = 0
  
      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 SurrealDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: kps_2d,  kps_3d,  rot,  diff1,  diff2,  scale