code: def update_main_process_results(self, trainer, extra) -> None:
      trainer.strategy.test_val = extra.pop("test_val")
      return super().update_main_process_results(trainer, extra)
  
imports: import os
  from unittest import mock
  from unittest.mock import Mock
  import lightning.pytorch
  import pytest
  import torch
  from lightning.fabric.plugins.environments import ClusterEnvironment, LightningEnvironment
  from lightning.fabric.utilities.distributed import _distributed_is_initialized
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
  from lightning.pytorch import Trainer
  from lightning.pytorch.callbacks import Callback, EarlyStopping
  from lightning.pytorch.demos.boring_classes import BoringDataModule, BoringModel
  from lightning.pytorch.strategies import DDPStrategy
  from lightning.pytorch.strategies.launchers import _SubprocessScriptLauncher
  from lightning.pytorch.strategies.launchers.multiprocessing import _MultiProcessingLauncher
  from lightning.pytorch.trainer import seed_everything
  from torch.distributed.optim import ZeroRedundancyOptimizer
  from torch.multiprocessing import ProcessRaisedException
  from torch.nn.parallel.distributed import DistributedDataParallel
  import tests_pytorch.helpers.pipelines
  from tests_pytorch.helpers.datamodules import ClassifDataModule
  from tests_pytorch.helpers.runif import RunIf
  from tests_pytorch.helpers.simple_models import ClassificationModel
label_desc: Use image node_id 2 for calling the CustomMultiProcessingLauncher obj's underlying member method code with example usage: obj.update_main_process_results(trainer, extra) and returns: super