code: def test_dataset_for_text_tokens_distributed_num_workers(tmpdir):
      seed_everything(42)
  
      block_size = 10
      cache = Cache(
          input_dir=str(tmpdir),
          chunk_size=40,
          item_loader=TokensLoader(block_size),
      )
  
      counter = 0
      for i in range(10):
          text_ids = torch.arange(counter, counter + 20).to(torch.int)
          cache[i] = text_ids
          counter += 20
  
      cache.done()
      cache.merge()
  
      for i in range(20):
          sequence = cache[i]
          assert sequence[0].item() == i * block_size
          assert sequence[-1].item() == (i + 1) * block_size - 1
  
      assert (
          len([f for f in os.listdir(tmpdir) if f.endswith(".bin")])
          == 5
      )
  
      dataset = StreamingDataset(
          input_dir=str(tmpdir),
          item_loader=TokensLoader(block_size),
          shuffle=False,
      )
  
      assert len(dataset) == 20
  
      dataset.distributed_env = _DistributedEnv(2, 0)
      dataloader = DataLoader(
          dataset, batch_size=2, shuffle=False, num_workers=2
      )
  
      assert len(dataloader) == 6
  
      expected = [
          [0, 10],
          [80, 90],
          [20, 30],
          [100, 110],
          [160, 170],
          [180, 190],
      ]
  
      for batch_idx, batch in enumerate(dataloader):
          assert [batch[0][0].item(), batch[1][0].item()] == expected[
              batch_idx
          ]
  
      dataset.distributed_env = _DistributedEnv(2, 1)
      dataloader = DataLoader(dataset, batch_size=2, shuffle=False)
  
      assert len(dataloader) == 4
  
      expected = [[40, 50], [60, 70], [120, 130], [140, 150]]
  
      for batch_idx, batch in enumerate(dataloader):
          assert [batch[0][0].item(), batch[1][0].item()] == expected[
              batch_idx
          ]
  
      for batch_idx, batch in enumerate(dataloader):
          assert [batch[0][0].item(), batch[1][0].item()] == expected[
              batch_idx
          ]
  
imports: import os
  import sys
  from time import sleep
  from unittest import mock
  import numpy
  import pytest
  import torch
  from lightning import seed_everything
  from lightning.data.streaming import Cache, functions
  from lightning.data.streaming.dataloader import StreamingDataLoader
  from lightning.data.streaming.dataset import _INDEX_FILENAME, Dir, RemoteDir, StreamingDataset, _associate_chunks_to_workers, _replay_chunks_sampling, _replay_sampling, _should_replace_path, _try_create_cache_dir
  from lightning.data.streaming.item_loader import TokensLoader
  from lightning.data.streaming.shuffle import FullShuffle, NoShuffle
  from lightning.data.utilities.env import Environment, _DistributedEnv, _WorkerEnv
  from torch.utils.data import DataLoader
label_desc: Use image node_id 11 for calling a global function with example usage: test_dataset_for_text_tokens_distributed_num_workers(tmpdir) without return types