code: def check_dataloader_sharding():
      plugin = DPPluginWrapper()
  
      # create a custom dataset with 0 to 10
      dataset = TensorDataset(torch.arange(0, 10))
      train_dataloader = plugin.prepare_dataloader(
          dataset, batch_size=2
      )
  
      # get the first batch of data
      batch = next(iter(train_dataloader))[0].cuda()
      is_rank_0 = dist.get_rank() == 0
  
      if is_rank_0:
          batch_to_compare = batch.clone()
      else:
          batch_to_compare = batch
      # pass to the rank 1 value to rank 0
      dist.broadcast(batch_to_compare, src=1)
  
      # compare on rank 0
      if is_rank_0:
          assert not torch.equal(
              batch, batch_to_compare
          ), "Same number was found across ranks but expected it to be different"
  
imports: from typing import Callable, Iterator, List, Tuple, Union
  import torch
  import torch.distributed
  import torch.nn
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.utils.data import DataLoader, TensorDataset
  import colossalai
  from colossalai.booster.plugin.dp_plugin_base import DPPluginBase
  from colossalai.checkpoint_io import CheckpointIO
  from colossalai.interface import OptimizerWrapper
  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: check_dataloader_sharding() without return types