code: def test_transfer_weights(tmpdir, trainer_fn):
      """Tests that the multiprocessing launcher transfers the new weights to the main process and deletes the temporary
      file."""
      model = Mock(wraps=BoringModel(), spec=BoringModel)
      strategy = DDPStrategy(start_method="spawn")
      trainer = Trainer(
          accelerator="cpu", default_root_dir=tmpdir, strategy=strategy
      )
      trainer.strategy.connect(model)
      trainer.state.fn = (
          trainer_fn  # pretend we are in a particular trainer state
      )
  
      spawn_output = strategy._launcher._collect_rank_zero_results(
          trainer, {}
      )
  
      model.state_dict.assert_called_once()
      if trainer_fn == TrainerFn.FITTING:
          assert spawn_output.weights_path.endswith(".temp.ckpt")
          assert os.path.isfile(spawn_output.weights_path)
      else:
          assert spawn_output.weights_path is None
  
      # <-- here would normally be the multiprocessing boundary
      strategy._launcher._recover_results_in_main_process(
          spawn_output, trainer
      )
      assert model.load_state_dict.call_count == int(
          spawn_output.weights_path is not None
      )
  
imports: import os
  from multiprocessing import Process
  from unittest import mock
  from unittest.mock import ANY, Mock, call, patch
  import pytest
  import torch
  from lightning.fabric.plugins import ClusterEnvironment
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.strategies import DDPStrategy
  from lightning.pytorch.strategies.launchers.multiprocessing import _GlobalStateSnapshot, _MultiProcessingLauncher
  from lightning.pytorch.trainer.states import TrainerFn
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 6 for calling a global function with example usage: test_transfer_weights(tmpdir, trainer_fn) without return types