code: def test_suggested_max_num_workers(
      cpu_count_mock,
      affinity,
      cpu_count,
      local_world_size,
      expected,
      monkeypatch,
  ):
      if affinity:
          monkeypatch.setattr(
              lightning.fabric.utilities.data.os,
              "sched_getaffinity",
              lambda _: list(range(cpu_count)),
          )
      else:
          monkeypatch.delattr(
              lightning.fabric.utilities.data.os,
              "sched_getaffinity",
              raising=False,
          )
          cpu_count_mock.return_value = cpu_count
  
      assert suggested_max_num_workers(local_world_size) == expected
  
imports: import contextlib
  import os
  import random
  from unittest import mock
  from unittest.mock import Mock
  import lightning.fabric
  import numpy
  import pytest
  import torch
  from lightning.fabric.utilities.data import AttributeDict, _get_dataloader_init_args_and_kwargs, _replace_dunder_methods, _replace_value_in_saved_args, _set_sampler_epoch, _update_dataloader, _WrapAttrTag, has_iterable_dataset, has_len, suggested_max_num_workers
  from lightning.fabric.utilities.exceptions import MisconfigurationException
  from lightning_utilities.test.warning import no_warning_call
  from torch import Tensor
  from torch.utils.data import BatchSampler, DataLoader, RandomSampler
  from tests_fabric.helpers.models import RandomDataset, RandomIterableDataset
label_desc: Use image node_id 17 for calling a global function with example usage: test_suggested_max_num_workers(cpu_count_mock, affinity, cpu_count, local_world_size, expected, monkeypatch) without return types