code: def _next_perm(self):
      self.seed += 1
      g = torch.Generator()
      g.manual_seed(self.seed)
      self._perm = torch.randperm(self.num_classes, generator=g)
  
imports: import contextlib
  import logging
  import queue
  from typing import Dict, List
  import numpy
  import torch
  from PIL import Image
  from torch.utils.data import Dataset
  from torch.utils.data.distributed import DistributedSampler
label_desc: Use image node_id 5 for calling the StratifiedClassSampler obj's underlying member method code with example usage: obj._next_perm() without return types