code: def __getitem__(self, index):
      if not self.seed_set:
          self.seed_set = True
          random.seed(index)
          np.random.seed(index)
  
      kps_3d = self.kp3ds[index]
      seq_len = self.sequence_lens[index]
      index_in_seq = self.indices_in_seq[index]
      interval = int((self.num_frames - 1) / 2) * self.frame_interval
      start, end = index_in_seq - interval, index_in_seq + interval
      kps_3d = self.kp3ds[index]
  
      kps_2d = self.kp2ds[index]
      if index_in_seq + 5 < seq_len:
          diff1_index = index + 5
      else:
          diff1_index = index - 5
  
      if self.frame_interval > 1:
          if index_in_seq + self.frame_interval < seq_len:
              diff1_index = index + self.frame_interval
          else:
              diff1_index = index - self.frame_interval
  
      if self.exp_tmc:
          if (
              index_in_seq
              + (self.exp_tmc_start + self.exp_tmc_interval)
              < seq_len
          ):
              diff1_index = index + (
                  np.random.randint(
                      self.exp_tmc_start,
                      self.exp_tmc_start + self.exp_tmc_interval,
                  )
                  if not self.exp_tmc_deterministic
                  else self.exp_tmc_interval
              )
          else:
              diff1_index = index - (
                  np.random.randint(
                      self.exp_tmc_start,
                      self.exp_tmc_start + self.exp_tmc_interval,
                  )
                  if not self.exp_tmc_deterministic
                  else self.exp_tmc_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
          )
      diff_index = index + diff_dist
      diff2 = self.kp2ds[diff_index]
  
      if not self.online_rot:
          rot = self.rotation[index]
      else:
          rot = np.random.rand(
              3,
          ) * np.array([self.bound_y, self.bound_x, self.bound_z])
          rot = torch.from_numpy(rot).float()
      # the flag will always be 1 when no extra data is used
      # for valdiation, simply ignore scale
      if self.scales is not None and self.is_train:
          scale = self.scales[index]
      else:
          scale = 0
      seqname = self.sequence_names[index]
      seqindex = self.seqname2seqindex[seqname]
  
      if self.is_15joints:
          kps_2d = kps_2d[self.map_to_15joints]
          kps_3d = kps_3d[self.map_to_15joints]
          diff1 = diff1[self.map_to_15joints]
          diff2 = diff2[self.map_to_15joints]
  
      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, save_pickle, load_pickle
label_desc: Use image node_id 5 for calling the Human36MDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: kps_2d,  kps_3d,  rot,  diff1,  diff2,  scale