code: def setup_distributed_dataloader(
      dataset: DatasetType,
      batch_size: int = 1,
      shuffle: bool = False,
      seed: int = 1024,
      drop_last: bool = False,
      pin_memory: bool = False,
      num_workers: int = 0,
      collate_fn: Callable[
          [Sequence[Dict[str, Union[str, List[int]]]]],
          Dict[str, torch.Tensor],
      ] = None,
      process_group: Optional[ProcessGroup] = None,
      **kwargs,
  ) -> DataLoader:
      """
      Setup dataloader for distributed training.
      """
      _kwargs = kwargs.copy()
      process_group = process_group or _get_default_group()
      sampler = StatefulDistributedSampler(
          dataset=dataset,
          num_replicas=process_group.size(),
          rank=process_group.rank(),
          shuffle=shuffle,
          seed=seed,
          drop_last=drop_last,
      )
  
      # Deterministic dataloader
      def seed_worker(worker_id: int) -> None:
          worker_seed = seed
          np.random.seed(worker_seed)
          torch.manual_seed(worker_seed)
          random.seed(worker_seed)
  
      return DataLoader(
          dataset=dataset,
          batch_size=batch_size,
          sampler=sampler,
          num_workers=num_workers,
          collate_fn=collate_fn,
          pin_memory=pin_memory,
          drop_last=drop_last,
          worker_init_fn=seed_worker,
          **_kwargs,
      )
  
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 2 for calling a global function with example usage: setup_distributed_dataloader(dataset, batch_size, shuffle, seed, drop_last, pin_memory, num_workers, collate_fn, process_group) and returns: DataLoader