class_docstr: Strategy for training multiple TPU devices using the :func:`torch_xla.distributed.xla_multiprocessing.spawn`
  method.
code: def checkpoint_io(self) -> XLACheckpointIO:
      plugin = self._checkpoint_io
      if plugin is not None:
          assert isinstance(plugin, XLACheckpointIO)
          return plugin
      return XLACheckpointIO()
  
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 4 for calling the XLAStrategy obj's underlying member method code with example usage: obj.checkpoint_io() and returns: XLACheckpointIO, plugin