class_docstr: Strategy for training on a single XLA device.
code: def precision_plugin(
      self, precision_plugin: Optional[XLAPrecision]
  ) -> None:
      if precision_plugin is not None and not isinstance(
          precision_plugin, XLAPrecision
      ):
          raise TypeError(
              f"The XLA strategy can only work with the `XLAPrecision` plugin, found {precision_plugin}"
          )
      self._precision_plugin = precision_plugin
  
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 5 for calling the SingleDeviceXLAStrategy obj's underlying member method code with example usage: obj.precision_plugin(precision_plugin) without return types