class_docstr: Stateful distributed sampler for multi-stage training.
code: def set_start_index(self, start_index: int) -> None:
      self.start_index = start_index
  
imports: import numpy
  import os
  import random
  from dataclasses import dataclass
  from typing import Dict, List, Union, Sequence, Optional, Iterator, Callable
  import torch
  from datasets import dataset_dict, load_from_disk
  from datasets import Dataset
  from torch.distributed import ProcessGroup
  from torch.distributed.distributed_c10d import _get_default_group
  from torch.utils.data import ConcatDataset, Dataset, DataLoader, DistributedSampler
  from transformers.tokenization_utils import PreTrainedTokenizer
  import torch.nn.functional
label_desc: Use image node_id 4 for calling the StatefulDistributedSampler obj's underlying member method code with example usage: obj.set_start_index(start_index) without return types