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","."]
docstr: Save model/training states as a checkpoint file through state-dump and file-write.
  
  Args:
      checkpoint: dict containing model and trainer state
      path: write-target path
      storage_options: not used in ``XLACheckpointIO.save_checkpoint``
  
  Raises:
      TypeError:
          If ``storage_options`` arg is passed in
docstr_tok: ["Save","model\/training","states","as","a","checkpoint","file","through","state-dump","and","file-write",".","Args",":","checkpoint",":","dict","containing","model","and","trainer","state","path",":","write-target","path","storage_options",":","not","used","in","``","XLACheckpointIO.save_checkpoint","``","Raises",":","TypeError",":","If","``","storage_options","``","arg","is","passed","in"]
code: def save_checkpoint(
      self,
      checkpoint: Dict[str, Any],
      path: _PATH,
      storage_options: Optional[Any] = None,
  ) -> None:
      """Save model/training states as a checkpoint file through state-dump and file-write.
  
      Args:
          checkpoint: dict containing model and trainer state
          path: write-target path
          storage_options: not used in ``XLACheckpointIO.save_checkpoint``
  
      Raises:
          TypeError:
              If ``storage_options`` arg is passed in
  
      """
      if storage_options is not None:
          raise TypeError(
              "`Trainer.save_checkpoint(..., storage_options=...)` with `storage_options` arg"
              f" is not supported for `{self.__class__.__name__}`. Please implement your custom `CheckpointIO`"
              " to define how you'd like to use `storage_options`."
          )
      fs = get_filesystem(path)
      fs.makedirs(os.path.dirname(path), exist_ok=True)
      if RequirementCache("omegaconf"):
          # workaround for https://github.com/pytorch/xla/issues/2773
          from omegaconf import DictConfig, ListConfig, OmegaConf
  
          checkpoint = apply_to_collection(
              checkpoint,
              (DictConfig, ListConfig),
              OmegaConf.to_container,
          )
      import torch_xla.core.xla_model as xm
  
      cpu_data = xm._maybe_convert_to_cpu(checkpoint, convert=True)
      log.debug(f"Saving checkpoint: {path}")
      torch.save(cpu_data, path)
  
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 2 for calling the XLACheckpointIO obj's underlying member method code with example usage: obj.save_checkpoint(checkpoint, path, storage_options) without return types