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
      )
  
      envs = schedule_processors(self._strategy.num_processes)
      cpu_procs = self._strategy.cpu_for_each_process
      if cpu_procs is not None:
          for i in range(len(cpu_procs)):
              envs[i]["KMP_AFFINITY"] = (
                  f"granularity=fine,proclist"
                  + f"=[{','.join(map(str, cpu_procs[i]))}],explicit"
              )
              envs[i]["OMP_NUM_THREADS"] = str(len(cpu_procs[i]))
  
      # the `return_queue` is necessary for recovering child process's state, we need
      # to dump it in this process and load it in subprocess, the `mp.SimpleQueue()` in
      # `ddp_spawn.py` cannot be dumped, so we use `multiprocessing.Manager().Queue()` here,
      # however, in order to be able to load it in subprocess, we must ensure the
      # `current_process().authkey` in this process and in subprocess are the same
      authkey = str(uuid.uuid1())
      multiprocessing.current_process().authkey = bytes(
          authkey, encoding="utf-8"
      )
      mp = multiprocessing.Manager()
  
      with TemporaryDirectory() as temp_dir:
          return_queue = mp.Queue()
          error_queue = mp.Queue()
          args = (trainer, function, args, kwargs, return_queue)
  
          # when using trainer, if we dump `trainer` and `self._wrapping_function` at the
          # same time, then after we load them in the subprocess, the loaded `trainer` may
          # be different from the one we dumped sometimes. so now, when using trianer, we
          # don't dump the `self._wrapping_function` and access it through `trainer` in
          # subprocess, when using LightningLite, the `trainer` is None, so we must dump
          # `self._wrapping_function`.
          with open(os.path.join(temp_dir, "args.pkl"), "wb") as f:
              if trainer is not None:
                  cloudpickle.dump((None, args, error_queue), f)
              else:
                  cloudpickle.dump(
                      (self._wrapping_function, args, error_queue), f
                  )
  
          # we also need to pass sys.path to subprocess
          with open(os.path.join(temp_dir, "sys_path.json"), "w") as f:
              json.dump(sys.path, f)
  
          with open(
              os.path.join(temp_dir, "patch_status.json"), "w"
          ) as f:
              json.dump(_get_patch_status(), f)
  
          processes = []
          cwd_path = os.path.split(os.path.realpath(__file__))[0]
          for i in range(self._strategy.num_processes):
              envs[i]["AUTHKEY"] = authkey
              processes.append(
                  subprocess.Popen(
                      [
                          sys.executable,
                          f"{cwd_path}/worker.py",
                          temp_dir,
                      ],
                      env=envs[i],
                  )
              )
  
          for _, process in enumerate(processes):
              process.wait()
          for _, process in enumerate(processes):
              if process.returncode != 0:
                  if not error_queue.empty():
                      invalidInputError(False, f"{error_queue.get()}")
                  else:
                      invalidInputError(
                          False, "subprocess exits incorrectly"
                      )
  
          # 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 cloudpickle
  import os
  import multiprocessing
  import subprocess
  import sys
  import uuid
  import json
  from typing import Any, Optional, Callable
  from tempfile import TemporaryDirectory
  import pytorch_lightning
  from bigdl.nano.pytorch.strategies.ddp_spawn import DDPSpawnStrategy, _DDPSpawnLauncher
  from bigdl.nano.utils.common import schedule_processors
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.pytorch.dispatcher import _get_patch_status
  import logging
label_desc: Use image node_id 2 for calling the _DDPSubprocessLauncher obj's underlying member method code with example usage: obj.launch(function) and returns: spawn_output, spawn_output