code: def __iter__(self):
      num_classes = len(self.data_source.dataset)
      num_classes_per_task = self.data_source.num_classes_per_task
      for _ in combinations(range(num_classes), num_classes_per_task):
          yield tuple(
              random.sample(range(num_classes), num_classes_per_task)
          )
  
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 2 for calling the CombinationRandomSampler obj's underlying member method code with example usage: obj.__iter__() without return types