class_docstr: ReproducibleShuffleSampler will apply a deterministic shuffle based on a seed.
  
  .. warning::
  
     Always shuffle before skipping and before repeating.  Skip-before-shuffle would break
     the reproducibility of the shuffle, and repeat-before-shuffle would cause the shuffle
     to hang as it iterates through an infinite sampler.
class_docstr_tok: ["ReproducibleShuffleSampler","will","apply","a","deterministic","shuffle","based","on","a","seed",".","..","warning",":",":","Always","shuffle","before","skipping","and","before","repeating",".","Skip-before-shuffle","would","break","the","reproducibility","of","the","shuffle",",","and","repeat-before-shuffle","would","cause","the","shuffle","to","hang","as","it","iterates","through","an","infinite","sampler","."]
code: def __init__(
      self, sampler: torch.utils.data.Sampler, seed: int
  ) -> None:
      self._sampler = sampler
      self._rng = np.random.RandomState(seed)
  
imports: from typing import Iterator
  import numpy
  import torch
label_desc: Use image node_id 1 to create a new ReproducibleShuffleSampler object from inherited base classes: torch.utils.data with example: obj = ReproducibleShuffleSampler(sampler, seed)