code: def run_data_sampler(rank, world_size, port):
      dist_args = dict(
          config=CONFIG,
          rank=rank,
          world_size=world_size,
          backend="gloo",
          port=port,
          host="localhost",
      )
      colossalai.legacy.launch(**dist_args)
  
      # build dataset
      transform_pipeline = [
          transforms.ToTensor(),
          transforms.RandomCrop(size=32, padding=4),
      ]
      transform_pipeline = transforms.Compose(transform_pipeline)
      dataset = datasets.CIFAR10(
          root=Path(os.environ["DATA"]),
          train=True,
          download=True,
          transform=transform_pipeline,
      )
  
      # build dataloader
      dataloader = get_dataloader(
          dataset, batch_size=8, add_sampler=False
      )
  
      data_iter = iter(dataloader)
      img, label = data_iter.next()
      img = img[0]
  
      if gpc.get_local_rank(ParallelMode.DATA) != 0:
          img_to_compare = img.clone()
      else:
          img_to_compare = img
      dist.broadcast(
          img_to_compare, src=0, group=gpc.get_group(ParallelMode.DATA)
      )
  
      if gpc.get_local_rank(ParallelMode.DATA) != 0:
          # this is without sampler
          # this should be false if data parallel sampler to given to the dataloader
          assert torch.equal(
              img, img_to_compare
          ), "Same image was distributed across ranks and expected it to be the same"
      torch.cuda.empty_cache()
  
file_path: ColossalAI/tests/test_legacy/test_data/test_deterministic_dataloader.py
imports: import os
  from pathlib import Path
  import torch
  import torch.distributed
  from torchvision import datasets, transforms
  import colossalai
  from colossalai.context import Config
  from colossalai.legacy.context import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.legacy.utils import get_dataloader
  from colossalai.testing import rerun_if_address_is_in_use, spawn
label_desc: Use image node_id 1 for calling a global function with example usage: run_data_sampler(rank, world_size, port) without return types