text: import math
  import torch
  from torch.utils.data import Sampler
  import numpy as np
  import torch.distributed as dist
  
  
  class DistributedPartialSampler(Sampler):
      """Based on pytorch DistributedSampler.
      It further sub-samples the dataset.
  
      Sampler that restricts data 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, percentage, num_replicas=None, rank=None):
          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(
              percentage * math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
          self.total_size = self.num_samples * self.num_replicas
          self.percentage = percentage
  
      def __iter__(self):
          # deterministically shuffle based on epoch
          g = torch.Generator()
          g.manual_seed(self.epoch)
          indices = torch.randperm(len(self.dataset), generator=g).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]
          indices = np.random.choice(
              indices, size=self.num_samples, replace=False)
          assert len(indices) == self.num_samples
  
          return iter(indices)
  
      def __len__(self):
          return self.num_samples
  
      def set_epoch(self, epoch):
          self.epoch = epoch
  