class_docstr: SkipSampler skips some records from an underlying Sampler, and yields the rest.
  
  Always skip before you repeat when you are continuing training, or you will apply the skip on
  every epoch.
  
  .. warning::
  
     When trying to achieve reproducibility after pausing and restarting, you should never prefer
     this SkipSampler over the SkipBatchSampler, unless you are sure that your dataset will always
     yield identically sized batches.  This is due to how Determined counts batches trained but
     does not count records trained.  Reproducibility when skipping records is only possible if
     the records to skip can be reliably calculated based on batch size and batches trained.
  
  Because the SkipSampler 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.
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code: def __len__(self) -> int:
      return len(self._sampler)
  
imports: from typing import Iterator
  import numpy
  import torch
label_desc: Use image node_id 2 for calling the SkipSampler obj's underlying member method code with example usage: obj.__len__() and returns: len