class_docstr: CheckpointIO that utilizes ``xm.save`` to save checkpoints for TPU training strategies.
  
  .. warning::  This is an :ref:`experimental <versioning:Experimental API>` feature.
class_docstr_tok: ["CheckpointIO","that","utilizes","``","xm.save","``","to","save","checkpoints","for","TPU","training","strategies",".","..","warning",":",":","This","is","an",":","ref",":","`","experimental","<","versioning",":","Experimental","API",">","`","feature","."]
code: def __init__(self, *args: Any, **kwargs: Any) -> None:
      if not _XLA_AVAILABLE:
          raise ModuleNotFoundError(str(_XLA_AVAILABLE))
      super().__init__(*args, **kwargs)
  
file_path: pytorch-lightning/src/lightning/fabric/plugins/io/xla.py
imports: import logging
  import os
  from typing import Any, Dict, Optional
  import torch
  from lightning_utilities.core.apply_func import apply_to_collection
  from lightning_utilities.core.imports import RequirementCache
  from typing_extensions import override
  from lightning.fabric.accelerators.xla import _XLA_AVAILABLE
  from lightning.fabric.plugins.io.torch_io import TorchCheckpointIO
  from lightning.fabric.utilities.cloud_io import get_filesystem
  from lightning.fabric.utilities.types import _PATH
label_desc: Use image node_id 1 to create a new XLACheckpointIO object from inherited base classes: TorchCheckpointIO with example: obj = XLACheckpointIO()