class_docstr: SkipBatchSampler skips some batches from an underlying BatchSampler, and yield the rest.
  
  Always skip before you repeat when you are continuing training, or you will apply the skip on
  every epoch.
  
  Because the SkipBatchSampler is only meant to be used on a training dataset (we never
  checkpoint during evaluation), and because the training dataset should always be repeated
  before applying the skip (so you only skip once rather than many times), the length reported
  is always the length of the underlying sampler, regardless of the size of the skip.
class_docstr_tok: ["SkipBatchSampler","skips","some","batches","from","an","underlying","BatchSampler",",","and","yield","the","rest",".","Always","skip","before","you","repeat","when","you","are","continuing","training",",","or","you","will","apply","the","skip","on","every","epoch",".","Because","the","SkipBatchSampler","is","only","meant","to","be","used","on","a","training","dataset","(","we","never","checkpoint","during","evaluation",")",",","and","because","the","training","dataset","should","always","be","repeated","before","applying","the","skip","(","so","you","only","skip","once","rather","than","many","times",")",",","the","length","reported","is","always","the","length","of","the","underlying","sampler",",","regardless","of","the","size","of","the","skip","."]
code: def __len__(self) -> int:
      return len(self.batch_sampler)
  
imports: from typing import Iterator
  import numpy
  import torch
label_desc: Use image node_id 2 for calling the SkipBatchSampler obj's underlying member method code with example usage: obj.__len__() and returns: len