class_docstr: RepeatBatchSampler yields infinite batches indices by repeatedly iterating
  through the batches of another BatchSampler. __len__ is just the length of
  the underlying BatchSampler.
class_docstr_tok: ["RepeatBatchSampler","yields","infinite","batches","indices","by","repeatedly","iterating","through","the","batches","of","another","BatchSampler",".","__len__","is","just","the","length","of","the","underlying","BatchSampler","."]
code: def __init__(
      self, batch_sampler: torch.utils.data.BatchSampler
  ) -> None:
      self.batch_sampler = batch_sampler
  
imports: from typing import Iterator
  import numpy
  import torch
label_desc: Use image node_id 1 to create a new RepeatBatchSampler object from inherited base classes: torch.utils.data with example: obj = RepeatBatchSampler(batch_sampler)