content: # Code is copy-pasted exactly as in torch.utils.dataset.distributed.
  # FIXME remove this once c10d fixes the bug it has
  import math
  import torch
  import torch.distributed as dist
  from torch.utils.data.sampler import Sampler
  
  
  class DistributedSampler(Sampler):
      """Sampler that restricts dataset loading to a subset of the dataset.
      It is especially useful in conjunction with
      :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
      process can pass a DistributedSampler instance as a DataLoader sampler,
      and load a subset of the original dataset that is exclusive to it.
      .. note::
          Dataset is assumed to be of constant size.
      Arguments:
          dataset: Dataset used for sampling.
          num_replicas (optional): Number of processes participating in
              distributed training.
          rank (optional): Rank of the current process within num_replicas.
      """
  
      def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
          if num_replicas is None:
              if not dist.is_available():
                  raise RuntimeError("Requires distributed package to be available")
              num_replicas = dist.get_world_size()
          if rank is None:
              if not dist.is_available():
                  raise RuntimeError("Requires distributed package to be available")
              rank = dist.get_rank()
          self.dataset = dataset
          self.num_replicas = num_replicas
          self.rank = rank
          self.epoch = 0
          self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
          self.total_size = self.num_samples * self.num_replicas
          self.shuffle = shuffle
  
      def __iter__(self):
          if self.shuffle:
              # deterministically shuffle based on epoch
              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
          offset = self.num_samples * self.rank
          indices = indices[offset : offset + self.num_samples]
          assert len(indices) == self.num_samples
  
          return iter(indices)
  
      def __len__(self):
          return self.num_samples
  
      def set_epoch(self, epoch):
          self.epoch = epoch
  