text: import torch
  from torch.utils.data import DistributedSampler as _DistributedSampler
  
  
  class DistributedSampler(_DistributedSampler):
      """DistributedSampler inheriting from
      ``torch.utils.data.DistributedSampler``.
  
      In pytorch of lower versions, there is no ``shuffle`` argument. This child
      class will port one to DistributedSampler.
      """
  
      def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
          super().__init__(dataset, num_replicas=num_replicas, rank=rank)
          self.shuffle = shuffle
  
      def __iter__(self):
          # deterministically shuffle based on epoch
          if self.shuffle:
              g = torch.Generator()
              g.manual_seed(self.epoch)
              indices = torch.randperm(len(self.dataset), generator=g).tolist()
          else:
              indices = torch.arange(len(self.dataset)).tolist()
  
          # add extra samples to make it evenly divisible
          indices += indices[:(self.total_size - len(indices))]
          assert len(indices) == self.total_size
  
          # subsample
          indices = indices[self.rank:self.total_size:self.num_replicas]
          assert len(indices) == self.num_samples
          return iter(indices)
  