class_docstr: Strategy for training on a single XLA device.
class_docstr_tok: ["Strategy","for","training","on","a","single","XLA","device","."]
code: def precision(self) -> XLAPrecision:
      plugin = self._precision
      if plugin is not None:
          assert isinstance(plugin, XLAPrecision)
          return plugin
      return XLAPrecision("32-true")
  
file_path: pytorch-lightning/src/lightning/fabric/strategies/single_xla.py
imports: from typing import Optional
  import torch
  from typing_extensions import override
  from lightning.fabric.accelerators import Accelerator
  from lightning.fabric.accelerators.xla import _XLA_AVAILABLE
  from lightning.fabric.plugins import XLAPrecision
  from lightning.fabric.plugins.io.xla import XLACheckpointIO
  from lightning.fabric.strategies import _StrategyRegistry
  from lightning.fabric.strategies.single_device import SingleDeviceStrategy
  from lightning.fabric.utilities.types import _DEVICE
label_desc: Use image node_id 4 for calling the SingleDeviceXLAStrategy obj's underlying member method code with example usage: obj.precision() and returns: XLAPrecision, plugin