code: def launch(
      self,
      function: Callable,
      *args: Any,
      trainer: Optional["pl.Trainer"] = None,
      **kwargs: Any,
  ) -> Any:
      # pytorch_lightning 1.6 uses this method to create child processes
      strategy = self._strategy
      strategy._setup_env_vars()
      strategy.global_to_local = strategy.get_local_ranks()  # type: ignore
  
      torch_backend = os.getenv("PL_TORCH_DISTRIBUTED_BACKEND")
      if torch_backend is not None:
          strategy._process_group_backend = torch_backend
  
      patch_status = _get_patch_status()
  
      futures = [
          strategy.workers[i].execute.remote(
              self._wrapping_function,
              (
                  i,
                  trainer,
                  strategy,
                  function,
                  args,
                  kwargs,
                  patch_status,
              ),
          )
          for i in range(strategy.num_workers)
      ]
  
      results = ray.get(futures)  # type: ignore
      ray.shutdown()  # release the resources occupied by ray
  
      # when using pytorch lightning's trainer, the `trainer` cannot be None,
      # when using pytorch lightning's LightningLite, the `trainer` should be None
      if trainer is None:
          return results[0]
  
      self._recover_results_in_main_process(results[0], trainer)
  
      return results[0].trainer_results
  
imports: import os
  import warnings
  from typing import Callable, Dict, List, Union, Any, Optional
  from collections import defaultdict
  import ray
  from ray.util.ml_utils.util import find_free_port
  import torch
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.nn.parallel.distributed import DistributedDataParallel
  import pytorch_lightning
  from pytorch_lightning.core.datamodule import LightningDataModule
  from pytorch_lightning.trainer.states import TrainerFn
  from pytorch_lightning.utilities import rank_zero_only
  from pytorch_lightning.utilities.apply_func import move_data_to_device
  from pytorch_lightning.strategies.launchers import _SpawnLauncher
  from pytorch_lightning.strategies import DDPSpawnStrategy
  from pytorch_lightning.core.optimizer import _configure_schedulers_automatic_opt
  from pytorch_lightning.core.optimizer import _configure_schedulers_manual_opt
  from pytorch_lightning.core.optimizer import _set_scheduler_opt_idx, _validate_scheduler_api
  from pytorch_lightning.core.optimizer import LightningOptimizer
  from .ray_envbase import RayEnvironment
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_12
  from bigdl.nano.deps.ipex.ipex_api import ipex_optimize
  from bigdl.nano.pytorch.dispatcher import _get_patch_status
label_desc: Use image node_id 3 for calling the _RayLauncher obj's underlying member method code with example usage: obj.launch(function) and returns: results, results