content: 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)
  
  
  class DistributedPowerSampler(_DistributedSampler):
      """DistributedPowerSampler inheriting from
      ``torch.utils.data.DistributedSampler``.
  
      Samples are sampled with the probability that is proportional to the power
      of label frequency (freq ^ power). The sampler only applies to single class
      recognition dataset.
  
      The default value of power is 1, which is equivalent to bootstrap sampling
      from the entire dataset.
      """
  
      def __init__(self, dataset, num_replicas=None, rank=None, power=1):
          super().__init__(dataset, num_replicas=num_replicas, rank=rank)
          self.power = power
  
      def __iter__(self):
          # deterministically shuffle based on epoch
          g = torch.Generator()
          g.manual_seed(self.epoch)
          video_infos_by_class = self.dataset.video_infos_by_class
          num_classes = self.dataset.num_classes
          # For simplicity, discontinuous labels are not permitted
          assert set(video_infos_by_class) == set(range(num_classes))
          counts = [len(video_infos_by_class[i]) for i in range(num_classes)]
          counts = [cnt**self.power for cnt in counts]
  
          indices = torch.multinomial(
              torch.Tensor(counts),
              self.total_size,
              replacement=True,
              generator=g)
          indices = indices.data.numpy().tolist()
          assert len(indices) == self.total_size
  
          indices = indices[self.rank:self.total_size:self.num_replicas]
          assert len(indices) == self.num_samples
  
          return iter(indices)
  