class_docstr: 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 a case, each
  process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a
  :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the
  original dataset that is exclusive to it.
  
  .. note::
      Dataset is assumed to be of constant size.
  
  Args:
      dataset: Dataset used for sampling.
      num_replicas (int, optional): Number of processes participating in
          distributed training. By default, :attr:`world_size` is retrieved from the
          current distributed group.
      rank (int, optional): Rank of the current process within :attr:`num_replicas`.
          By default, :attr:`rank` is retrieved from the current distributed
          group.
      shuffle (bool, optional): If ``True`` (default), sampler will shuffle the
          indices.
      seed (int, optional): random seed used to shuffle the sampler if
          :attr:`shuffle=True`. This number should be identical across all
          processes in the distributed group. Default: ``0``.
      drop_last (bool, optional): if ``True``, then the sampler will drop the
          tail of the data to make it evenly divisible across the number of
          replicas. If ``False``, the sampler will add extra indices to make
          the data evenly divisible across the replicas. Default: ``False``.
  
  .. warning::
      In distributed mode, calling the :meth:`set_epoch` method at
      the beginning of each epoch **before** creating the :class:`DataLoader` iterator
      is necessary to make shuffling work properly across multiple epochs. Otherwise,
      the same ordering will be always used.
  
  Example::
  
      >>> sampler = DistributedSampler(dataset) if is_distributed else None
      >>> loader = DataLoader(dataset, shuffle=(sampler is None),
      ...                     sampler=sampler)
      >>> for epoch in range(start_epoch, n_epochs):
      ...     if is_distributed:
      ...         sampler.set_epoch(epoch)
      ...     train(loader)
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code: def __init__(
      self,
      dataset,
      num_replicas: Optional[int] = None,
      rank: Optional[int] = None,
      shuffle: bool = True,
      seed: int = 0,
      drop_last: bool = False,
  ) -> None:
      import torch.distributed as dist
  
      if num_replicas is None:  # pragma: no cover
          if not dist.is_available():
              raise RuntimeError(
                  "Requires distributed package to be available"
              )
          num_replicas = dist.get_world_size()
      if rank is None:  # pragma: no cover
          if not dist.is_available():
              raise RuntimeError(
                  "Requires distributed package to be available"
              )
          rank = dist.get_rank()
      if rank >= num_replicas or rank < 0:
          raise ValueError(
              "Invalid rank {}, rank should be in the interval"
              " [0, {}]".format(rank, num_replicas - 1)
          )
  
      self.dataset = dataset
      self.num_replicas = num_replicas
      self.rank = rank
      self.epoch = 0
      self.drop_last = drop_last
      # If the dataset length is evenly divisible by # of replicas, then there
      # is no need to drop any data, since the dataset will be split equally.
      if self.drop_last and len(self.dataset) % self.num_replicas != 0:
          # Split to nearest available length that is evenly divisible.
          # This is to ensure each rank receives the same amount of data when
          # using this Sampler.
          self.num_samples = math.ceil(
              (len(self.dataset) - self.num_replicas)
              / self.num_replicas
          )
      else:
          self.num_samples = math.ceil(
              len(self.dataset) / self.num_replicas
          )
      self.total_size = self.num_samples * self.num_replicas
      self.shuffle = shuffle
      self.seed = seed
  
imports: from typing import Iterator, Optional, Sized, Sequence
  import math
  from ....utils import require_not_none
label_desc: Use image node_id 1 to create a new DistributedSampler object from inherited base classes: Sampler with example: obj = DistributedSampler(dataset, num_replicas, rank, shuffle, seed, drop_last)