class_docstr: Strategy for training multiple TPU devices using the :func:`torch_xla.distributed.xla_multiprocessing.spawn`
  method.
class_docstr_tok: ["Strategy","for","training","multiple","TPU","devices","using","the",":","func",":","`","torch_xla.distributed.xla_multiprocessing.spawn","`","method","."]
code: def checkpoint_io(
      self, io: Optional[Union[XLACheckpointIO, _WrappingCheckpointIO]]
  ) -> None:
      if io is not None and not isinstance(
          io, (XLACheckpointIO, _WrappingCheckpointIO)
      ):
          raise TypeError(
              f"The XLA strategy can only work with the `XLACheckpointIO` plugin, found {io}"
          )
      self._checkpoint_io = io
  
imports: import io
  import os
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
  import torch
  from torch import Tensor
  from torch.nn import Module
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.accelerators.xla import _XLA_AVAILABLE, _XLA_GREATER_EQUAL_2_1
  from lightning.fabric.plugins import XLACheckpointIO
  from lightning.fabric.plugins.environments import XLAEnvironment
  from lightning.fabric.strategies import _StrategyRegistry
  from lightning.fabric.utilities.optimizer import _optimizers_to_device
  from lightning.fabric.utilities.types import _PATH, ReduceOp
  from lightning.pytorch.plugins import XLAPrecision
  from lightning.pytorch.plugins.io.wrapper import _WrappingCheckpointIO
  from lightning.pytorch.strategies.ddp import DDPStrategy
  from lightning.pytorch.strategies.launchers.xla import _XLALauncher
  from lightning.pytorch.strategies.strategy import TBroadcast
  from lightning.pytorch.trainer.states import TrainerFn
  from lightning.pytorch.utilities import find_shared_parameters, set_shared_parameters
  from lightning.pytorch.utilities.rank_zero import rank_zero_only
label_desc: Use image node_id 3 for calling the XLAStrategy obj's underlying member method code with example usage: obj.checkpoint_io(io) without return types