class_docstr: Strategy for training multiple TPU devices using the :func:`torch_xla.distributed.xla_multiprocessing.spawn`
  method.
code: def __init__(
      self,
      accelerator: Optional[Accelerator] = None,
      parallel_devices: Optional[List[torch.device]] = None,
      checkpoint_io: Optional[XLACheckpointIO] = None,
      precision: Optional[XLAPrecision] = None,
      sync_module_states: bool = True,
  ) -> None:
      super().__init__(
          accelerator=accelerator,
          parallel_devices=parallel_devices,
          cluster_environment=XLAEnvironment(),
          checkpoint_io=checkpoint_io,
          precision=precision,
      )
      self._backward_sync_control = None  # XLA synchronizes gradients in the optimizer.step() call
      self._launched = False
      self._sync_module_states = sync_module_states
  
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 1 to create a new XLAStrategy object from inherited base classes: ParallelStrategy with example: obj = XLAStrategy(accelerator, parallel_devices, checkpoint_io, precision, sync_module_states)