code: def test_attach(event, n_calls, kwargs):
      n_epochs = 5
      data = list(range(50))
  
      losses = torch.rand(n_epochs * len(data))
      losses_iter = iter(losses)
  
      def update_fn(engine, batch):
          return next(losses_iter)
  
      trainer = Engine(update_fn)
  
      logger = DummyLogger()
  
      mock_log_handler = MagicMock()
  
      logger.attach(
          trainer,
          log_handler=mock_log_handler,
          event_name=event,
          **kwargs,
      )
  
      trainer.run(data, max_epochs=n_epochs)
  
      if isinstance(event, EventsList):
          events = [e for e in event]
      else:
          events = [event]
  
      if len(kwargs) > 0:
          calls = [call(trainer, logger, e, **kwargs) for e in events]
      else:
          calls = [call(trainer, logger, e) for e in events]
  
      mock_log_handler.assert_has_calls(calls)
      assert mock_log_handler.call_count == n_calls
  
imports: from typing import Any, Union
  from unittest.mock import call, MagicMock
  import pytest
  import torch
  from ignite.contrib.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler, BaseWeightsHandler, BaseWeightsScalarHandler
  from ignite.engine import Engine, Events, EventsList, State
  from tests.ignite.contrib.handlers import MockFP16DeepSpeedZeroOptimizer
label_desc: Use image node_id 5 for calling a global function with example usage: test_attach(event, n_calls, kwargs) and returns: next