code: def test_no_warning_with_train_sampler(recwarn):
      from torch.utils.data import RandomSampler
  
      trainer = Engine(lambda e, b: None)
      train_sampler = RandomSampler([0, 1, 2])
      setup_common_training_handlers(
          trainer, train_sampler=train_sampler
      )
      assert len(recwarn) == 0, recwarn.pop()
  
imports: import os
  import sys
  from unittest.mock import call, MagicMock
  import pytest
  import torch
  import torch.nn
  from torch.utils.data.distributed import DistributedSampler
  import ignite.contrib.handlers
  import ignite.distributed
  from ignite.contrib.engines.common import _setup_logging, add_early_stopping_by_val_score, gen_save_best_models_by_val_score, save_best_model_by_val_score, setup_any_logging, setup_clearml_logging, setup_common_training_handlers, setup_mlflow_logging, setup_neptune_logging, setup_plx_logging, setup_tb_logging, setup_trains_logging, setup_visdom_logging, setup_wandb_logging
  from ignite.engine import Engine, Events
  from ignite.handlers import DiskSaver, TerminateOnNan
label_desc: Use image node_id 3 for calling a global function with example usage: test_no_warning_with_train_sampler(recwarn) without return types