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 __iter__(self) -> Iterator:
      indices = list(self._sampler)
      self._rng.shuffle(indices)
      return iter(indices)
  
imports: from typing import Iterator
  import numpy
  import torch
label_desc: Use image node_id 2 for calling the ReproducibleShuffleSampler obj's underlying member method code with example usage: obj.__iter__() and returns: iter