class_docstr: Strategy for training on a single XLA device.
code: def checkpoint_io(
      self,
  ) -> Union[XLACheckpointIO, _WrappingCheckpointIO]:
      plugin = self._checkpoint_io
      if plugin is not None:
          assert isinstance(
              plugin, (XLACheckpointIO, _WrappingCheckpointIO)
          )
          return plugin
      return XLACheckpointIO()
  
imports: import os
  from typing import Optional, Union
  import torch
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.accelerators.xla import _XLA_AVAILABLE
  from lightning.fabric.plugins import XLACheckpointIO
  from lightning.fabric.strategies import _StrategyRegistry
  from lightning.fabric.utilities.types import _DEVICE
  from lightning.pytorch.plugins.io.wrapper import _WrappingCheckpointIO
  from lightning.pytorch.plugins.precision.xla import XLAPrecision
  from lightning.pytorch.strategies.single_device import SingleDeviceStrategy
  from lightning.pytorch.utilities import find_shared_parameters, set_shared_parameters
label_desc: Use image node_id 2 for calling the SingleDeviceXLAStrategy obj's underlying member method code with example usage: obj.checkpoint_io() and returns: XLACheckpointIO, plugin