class_docstr: Strategy for training on a single XLA device.
code: def __init__(
      self,
      device: _DEVICE,
      accelerator: Optional["pl.accelerators.Accelerator"] = None,
      checkpoint_io: Optional[
          Union[XLACheckpointIO, _WrappingCheckpointIO]
      ] = None,
      precision_plugin: Optional[XLAPrecision] = None,
      debug: bool = False,
  ):
      if not _XLA_AVAILABLE:
          raise ModuleNotFoundError(str(_XLA_AVAILABLE))
      if isinstance(device, torch.device):
          # unwrap the `torch.device` in favor of `xla_device`
          device = device.index
      import torch_xla.core.xla_model as xm
  
      super().__init__(
          accelerator=accelerator,
          device=xm.xla_device(device),
          checkpoint_io=checkpoint_io,
          precision_plugin=precision_plugin,
      )
      self.debug = debug
  
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 1 to create a new SingleDeviceXLAStrategy object from inherited base classes: SingleDeviceStrategy with example: obj = SingleDeviceXLAStrategy(device, accelerator, checkpoint_io, precision_plugin, debug)