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
class_docstr_tok: ["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"]
code: def _collect_rank_zero_results(
      self, trainer: "pl.Trainer", results: Any
  ) -> Optional["_WorkerOutput"]:
      rank_zero_debug("Collecting results from rank 0 process.")
      checkpoint_callback = trainer.checkpoint_callback
      best_model_path = (
          checkpoint_callback.best_model_path
          if checkpoint_callback
          and hasattr(checkpoint_callback, "best_model_path")
          else None
      )
  
      # save the last weights
      weights_path = None
      if trainer.state.fn == TrainerFn.FITTING:
          # requires to compute the state_dict on all processes in case Metrics are present
          state_dict = self._strategy.lightning_module_state_dict()
          weights_path = os.path.join(
              trainer.default_root_dir, ".temp.ckpt"
          )
          self._strategy.checkpoint_io.save_checkpoint(
              state_dict, weights_path
          )
  
      # We use `local_rank` here as separate filesystems are used for each VM for TPU Pod Training
      if self._strategy.local_rank != 0:
          return None
  
      # add extra result data from trainer to send to main process
      extra = self.get_extra_results(trainer)
  
      return _WorkerOutput(
          best_model_path, weights_path, trainer.state, results, extra
      )
  
file_path: pytorch-lightning/src/lightning/pytorch/strategies/launchers/xla.py
imports: import os
  import queue
  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.xla import _rank_teardown
  from lightning.fabric.utilities import move_data_to_device
  from lightning.pytorch.strategies.launchers.multiprocessing import _GlobalStateSnapshot, _MultiProcessingLauncher, _WorkerOutput
  from lightning.pytorch.trainer.states import TrainerFn
  from lightning.pytorch.utilities.rank_zero import rank_zero_debug
label_desc: Use image node_id 5 for calling the _XLALauncher obj's underlying member method code with example usage: obj._collect_rank_zero_results(trainer, results) and returns: _WorkerOutput, None