class_docstr: Strategy for training multiple TPU devices using the :func:`torch_xla.distributed.xla_multiprocessing.spawn`
  method.
code: def checkpoint_io(self, io: Optional[XLACheckpointIO]) -> None:
      if io is not None and not isinstance(io, XLACheckpointIO):
          raise TypeError(
              f"The XLA strategy can only work with the `XLACheckpointIO` plugin, found {io}"
          )
      self._checkpoint_io = io
  
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 5 for calling the XLAStrategy obj's underlying member method code with example usage: obj.checkpoint_io(io) without return types