code: def __next__(self) -> Any:
      # randomly select a dataset index
      (dataset_index,) = self._rng.choices(
          self._dataset_indexes, weights=self._weights, k=1
      )
  
      # keep track the sample was fetched
      self._num_samples_yielded[dataset_index] += 1
  
      # return a new sample
      return next(self._dataset_iters[dataset_index])
  
file_path: pytorch-lightning/src/lightning/data/streaming/combined.py
imports: import random
  from typing import Any, Dict, Iterator, List, Optional, Sequence
  from torch.utils.data import IterableDataset
  from lightning.data.streaming.dataset import StreamingDataset
label_desc: Use image node_id 2 for calling the _CombinedDatasetIterator obj's underlying member method code with example usage: obj.__next__() and returns: next