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
  
      # the `self._strategy.cluster_environment` should not be None in normal circumstances,
      # if you see this error message, please report an issue in BigDL.
      invalidInputError(
          self._strategy.cluster_environment is not None,
          "strategy.cluster_environment cannot be None",
      )
  
      os.environ["MASTER_PORT"] = str(
          self._strategy.cluster_environment.main_port
      )
  
      cpu_procs = self._strategy.cpu_for_each_process
      if cpu_procs is None:
          envs = schedule_processors(self._strategy.num_processes)
      else:
          envs = [
              {
                  "KMP_AFFINITY": f"granularity=fine,proclist"
                  f"=[{','.join([str(i) for i in cpu_procs[i]])}],explicit",
                  "OMP_NUM_THREADS": str(len(cpu_procs[i])),
              }
              for i in range(self._strategy.num_processes)
          ]
  
      mp = multiprocessing.get_context(self._start_method)
      return_queue = mp.SimpleQueue()
      error_queues = []
      processes = []
      args = (trainer, function, args, kwargs, return_queue)
      patch_status = _get_patch_status()
  
      for i in range(self._strategy.num_processes):
          with EnvContext(envs[i]):
              log.debug(
                  f"[Process {i}]: using KMP_AFFINITY: {os.environ['KMP_AFFINITY']}"
              )
              log.debug(
                  f"[Process {i}]: using OMP_NUM_THREADS: {os.environ['OMP_NUM_THREADS']}"
              )
              error_queue = mp.SimpleQueue()
              process = mp.Process(  # type: ignore
                  target=self._wrap,
                  args=(
                      self._wrapping_function,
                      i,
                      args,
                      error_queue,
                      patch_status,
                  ),
                  daemon=False,
              )
              process.start()
              error_queues.append(error_queue)
              processes.append(process)
  
      context = ProcessContext(processes, error_queues)
  
      while not context.join():
          pass
  
      # restore the state of child process
      spawn_output = return_queue.get()
  
      # 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 spawn_output
  
      self._recover_results_in_main_process(spawn_output, trainer)
      return spawn_output.trainer_results
  
imports: import os
  import copy
  import multiprocessing
  from typing import Any, List, Optional, Callable
  import torch
  from torch import nn
  from torch.multiprocessing.spawn import ProcessContext
  from torch.nn.parallel.distributed import DistributedDataParallel
  from torch.optim.lr_scheduler import _LRScheduler
  import pytorch_lightning
  from pytorch_lightning.trainer.states import TrainerFn
  from pytorch_lightning.core.optimizer import LightningOptimizer
  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.strategies.launchers import _SpawnLauncher
  from pytorch_lightning.strategies import DDPSpawnStrategy
  from pytorch_lightning.plugins.environments import LightningEnvironment
  from bigdl.nano.utils.common import schedule_processors
  from bigdl.nano.pytorch.dispatcher import _get_patch_status
  from bigdl.nano.deps.ipex.ipex_api import ipex_optimize
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.utils.common import EnvContext
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_12
  import logging
  import warnings
label_desc: Use image node_id 2 for calling the _DDPSpawnLauncher obj's underlying member method code with example usage: obj.launch(function) and returns: spawn_output, spawn_output