class_docstr: In training, we only care about the "infinite stream" of training data.
  So this sampler produces an infinite stream of indices and
  all workers cooperate to correctly shuffle the indices and sample different indices.
  
  The samplers in each worker effectively produces `indices[worker_id::num_workers]`
  where `indices` is an infinite stream of indices consisting of
  `shuffle(range(size)) + shuffle(range(size)) + ...` (if shuffle is True)
  or `range(size) + range(size) + ...` (if shuffle is False)
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docstr: Args:
      size (int): the total number of data of the underlying dataset to sample from
      shuffle (bool): whether to shuffle the indices or not
      seed (int): the initial seed of the shuffle. Must be the same
          across all workers. If None, will use a random seed shared
          among workers (require synchronization among all workers).
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code: def __init__(
      self, size: int, shuffle: bool = True, seed: Optional[int] = None
  ):
      """
      Args:
          size (int): the total number of data of the underlying dataset to sample from
          shuffle (bool): whether to shuffle the indices or not
          seed (int): the initial seed of the shuffle. Must be the same
              across all workers. If None, will use a random seed shared
              among workers (require synchronization among all workers).
      """
      self._size = size
      assert size > 0
      self._shuffle = shuffle
      if seed is None:
          seed = comm.shared_random_seed()
      self._seed = int(seed)
  
      self._rank = comm.get_rank()
      self._world_size = comm.get_world_size()
  
imports: import itertools
  import math
  from collections import defaultdict
  from typing import Optional
  import torch
  from torch.utils.data.sampler import Sampler
  from ....utils import comm
label_desc: Use image node_id 1 to create a new TrainingSampler object from inherited base classes: Sampler with example: obj = TrainingSampler(size, shuffle, seed)