code: def test_random_sampling_dataloader_with_empty_data(self) -> None:
      dataloader_1 = DataLoader(
          RandomDataset(size=32, length=0), batch_size=8
      )
      dataloader_2 = DataLoader(
          RandomDataset(size=32, length=64), batch_size=8
      )
  
      multi_dataloader = iter(
          MultiDataLoader(
              self._get_dataloaders_dict(dataloader_1, dataloader_2),
              RandomizedBatchSampler(
                  weights={"1": 1, "2": 100},
                  stopping_mechanism=StoppingMechanism.ALL_DATASETS_EXHAUSTED,
              ),
              ignore_empty_data=True,
          )
      )
  
      # The first dataloader is empty
      # It will constitute 25 batches in total
      selected_datasets = [
          next(multi_dataloader).popitem()[0] for _ in range(8)
      ]
      # check that 2 is the only one
      counts = Counter(selected_datasets)
      self.assertEqual(counts["2"], len(selected_datasets))
  
      # Now ensure that, without the `ignore_empty_data` flag, an exception is raised.
      with self.assertRaises(ValueError):
          MultiDataLoader(
              self._get_dataloaders_dict(dataloader_1, dataloader_2),
              RandomizedBatchSampler(weights={"1": 1, "2": 100}),
          )
  
imports: import random
  import unittest
  from collections import Counter
  from typing import Any, cast, Dict, Iterable, Iterator, List, Mapping, Union
  import torch
  from torch.utils.data import DataLoader, Dataset
  from torchtnt.utils.data.iterators import AllDatasetBatches, DataIterationStrategy, InOrder, MultiIterator, RandomizedBatchSampler, RoundRobin, StoppingMechanism
  from torchtnt.utils.data.multi_dataloader import MultiDataLoader
label_desc: Use image node_id 8 for calling the TestMultiDataLoader obj's underlying member method code with example usage: obj.test_random_sampling_dataloader_with_empty_data() without return types