class_docstr: Launches processes that run a given function in parallel on XLA supported hardware, and joins them all at the
  end.
  
  The main process in which this launcher is invoked creates N so-called worker processes (using the
  `torch_xla` :func:`xmp.spawn`) that run the given function.
  Worker processes have a rank that ranges from 0 to N - 1.
  
  Note:
      - This launcher requires all objects to be pickleable.
      - It is important that the entry point to the program/script is guarded by ``if __name__ == "__main__"``.
  
  Args:
      strategy: A reference to the strategy that is used together with this launcher
docstr: Launches processes that run the given function in parallel.
  
  The function is allowed to have a return value. However, when all processes join, only the return value
  of worker process 0 gets returned from this `launch` method in the main process.
  
  Arguments:
      function: The entry point for all launched processes.
      *args: Optional positional arguments to be passed to the given function.
      **kwargs: Optional keyword arguments to be passed to the given function.
docstr_tok: ["Launches","processes","that","run","the","given","function","in","parallel",".","The","function","is","allowed","to","have","a","return","value",".","However",",","when","all","processes","join",",","only","the","return","value","of","worker","process","0","gets","returned","from","this","`","launch","`","method","in","the","main","process",".","Arguments",":","function",":","The","entry","point","for","all","launched","processes",".","*","args",":","Optional","positional","arguments","to","be","passed","to","the","given","function",".","*","*","kwargs",":","Optional","keyword","arguments","to","be","passed","to","the","given","function","."]
code: def launch(
      self, function: Callable, *args: Any, **kwargs: Any
  ) -> Any:
      """Launches processes that run the given function in parallel.
  
      The function is allowed to have a return value. However, when all processes join, only the return value
      of worker process 0 gets returned from this `launch` method in the main process.
  
      Arguments:
          function: The entry point for all launched processes.
          *args: Optional positional arguments to be passed to the given function.
          **kwargs: Optional keyword arguments to be passed to the given function.
  
      """
      return_queue: Union[queue.Queue, mp.SimpleQueue]
      return_queue = mp.Manager().Queue()
  
      import torch_xla.distributed.xla_multiprocessing as xmp
  
      spawn_kwargs = {}
      nprocs = self._strategy.num_processes
      if nprocs == 1:
          # avoid warning: "Unsupported nprocs". If it's 1, it will call the launched function directly.
          # otherwise it will use all devices
          spawn_kwargs["nprocs"] = nprocs
  
      xmp.spawn(
          self._wrapping_function,
          args=(function, args, kwargs, return_queue),
          start_method=self._start_method,
          **spawn_kwargs,
      )
      return return_queue.get()
  
file_path: pytorch-lightning/src/lightning/fabric/strategies/launchers/xla.py
imports: import queue
  import time
  from typing import TYPE_CHECKING, Any, Callable, Optional, Union
  import torch.multiprocessing
  from typing_extensions import override
  from lightning.fabric.accelerators.xla import _XLA_AVAILABLE
  from lightning.fabric.strategies.launchers.launcher import _Launcher
  from lightning.fabric.strategies.launchers.multiprocessing import _GlobalStateSnapshot
  from lightning.fabric.utilities.apply_func import move_data_to_device
label_desc: Use image node_id 3 for calling the _XLALauncher obj's underlying member method code with example usage: obj.launch(function) and returns: return_queue