class_docstr: Extend Sampler to support "short cycle" sampling.
  See paper "A Multigrid Method for Efficiently Training Video Models",
  Wu et al., 2019 (https://arxiv.org/abs/1912.00998) for details.
code: def __iter__(self):
      counter = 0
      batch_size = self.batch_sizes[0]
      batch = []
      for idx in self.sampler:
          batch.append((idx, counter % 3))
          if len(batch) == batch_size:
              yield batch
              counter += 1
              batch_size = self.batch_sizes[counter % 3]
              batch = []
      if len(batch) > 0 and not self.drop_last:
          yield batch
  
imports: import numpy
  import torch
  from torch.utils.data.sampler import Sampler
label_desc: Use image node_id 2 for calling the ShortCycleBatchSampler obj's underlying member method code with example usage: obj.__iter__() without return types