class_docstr: Strategy for training multiple TPU devices using the :func:`torch_xla.distributed.xla_multiprocessing.spawn`
  method.
docstr: Save model, optimizer, and other state as a checkpoint file.
  
  Args:
      path: A path to where the file(s) should be saved
      state: A dictionary with contents to be saved. If the dict contains modules or optimizers, their
          state-dict will be retrieved and converted automatically.
      storage_options: Additional options for the ``CheckpointIO`` plugin
      filter: An optional dictionary of the same format as ``state`` mapping keys to callables that return a
          boolean indicating whether the given parameter should be saved (``True``) or filtered out (``False``).
docstr_tok: ["Save","model",",","optimizer",",","and","other","state","as","a","checkpoint","file",".","Args",":","path",":","A","path","to","where","the","file","(","s",")","should","be","saved","state",":","A","dictionary","with","contents","to","be","saved",".","If","the","dict","contains","modules","or","optimizers",",","their","state-dict","will","be","retrieved","and","converted","automatically",".","storage_options",":","Additional","options","for","the","``","CheckpointIO","``","plugin","filter",":","An","optional","dictionary","of","the","same","format","as","``","state","``","mapping","keys","to","callables","that","return","a","boolean","indicating","whether","the","given","parameter","should","be","saved","(","``","True","``",")","or","filtered","out","(","``","False","``",")","."]
code: def save_checkpoint(
      self,
      path: _PATH,
      state: Dict[str, Union[Module, Optimizer, Any]],
      storage_options: Optional[Any] = None,
      filter: Optional[Dict[str, Callable[[str, Any], bool]]] = None,
  ) -> None:
      """Save model, optimizer, and other state as a checkpoint file.
  
      Args:
          path: A path to where the file(s) should be saved
          state: A dictionary with contents to be saved. If the dict contains modules or optimizers, their
              state-dict will be retrieved and converted automatically.
          storage_options: Additional options for the ``CheckpointIO`` plugin
          filter: An optional dictionary of the same format as ``state`` mapping keys to callables that return a
              boolean indicating whether the given parameter should be saved (``True``) or filtered out (``False``).
  
      """
      import torch_xla.core.xla_model as xm
  
      # sync any pending lazy tensors on all ranks before saving to prevent potential collective hangs
      xm.mark_step()
      # save on global rank zero only
      super().save_checkpoint(
          path, state, storage_options=storage_options, filter=filter
      )
  
imports: import io
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
  import torch
  from torch import Tensor
  from torch.nn import Module
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader
  from typing_extensions import override
  from lightning.fabric.accelerators import Accelerator
  from lightning.fabric.accelerators.xla import _XLA_GREATER_EQUAL_2_1
  from lightning.fabric.plugins import XLAPrecision
  from lightning.fabric.plugins.environments import XLAEnvironment
  from lightning.fabric.plugins.io.xla import XLACheckpointIO
  from lightning.fabric.strategies import ParallelStrategy, _StrategyRegistry
  from lightning.fabric.strategies.launchers.xla import _XLALauncher
  from lightning.fabric.strategies.strategy import TBroadcast
  from lightning.fabric.utilities.rank_zero import rank_zero_only
  from lightning.fabric.utilities.types import _PATH, ReduceOp
label_desc: Use image node_id 21 for calling the XLAStrategy obj's underlying member method code with example usage: obj.save_checkpoint(path, state, storage_options, filter) without return types