code: def __init__(self, data_source):
      if not isinstance(data_source, CombinationMetaDataset):
          raise TypeError(
              "Expected `data_source` to be an instance of "
              "`CombinationMetaDataset`, but found "
              "{0}".format(type(data_source))
          )
      # Temporarily disable the warning if the length of the length of the
      # dataset exceeds the machine precision. This avoids getting this
      # warning shown with MetaDataLoader, even though MetaDataLoader itself
      # does not use the length of the dataset.
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          super(CombinationRandomSampler, self).__init__(
              data_source, replacement=True
          )
  
file_path: pytorch-meta/torchmeta/utils/data/sampler.py
imports: import random
  import warnings
  from itertools import combinations
  from torch.utils.data.sampler import SequentialSampler, RandomSampler
  from torchmeta.utils.data.dataset import CombinationMetaDataset
label_desc: Use image node_id 1 to create a new CombinationRandomSampler object from inherited base classes: RandomSampler with example: obj = CombinationRandomSampler(data_source)