code: def train_dataloader(self):
      dataset = torch.arange(64, dtype=torch.float).view(-1, 1)
      # we need to set a distributed sampler ourselves to force shuffle=False
      sampler = DistributedSampler(
          dataset,
          num_replicas=self.trainer.world_size,
          rank=self.trainer.global_rank,
          shuffle=False,
      )
      return DataLoader(
          dataset, sampler=sampler, batch_size=self.batch_size
      )
  
file_path: pytorch-lightning/tests/parity_pytorch/test_sync_batchnorm_parity.py
imports: import torch
  import torch.nn
  from lightning.pytorch import LightningModule, Trainer, seed_everything
  from torch.utils.data import DataLoader, DistributedSampler
  from parity_pytorch import RunIf
label_desc: Use image node_id 5 for calling the SyncBNModule obj's underlying member method code with example usage: obj.train_dataloader() and returns: DataLoader