class_docstr: CheckpointIO that utilizes :func:`torch.save` and :func:`torch.load` to save and load checkpoints respectively,
  common for most use cases.
  
  .. warning::  This is an :ref:`experimental <versioning:Experimental API>` feature.
class_docstr_tok: ["CheckpointIO","that","utilizes",":","func",":","`","torch.save","`","and",":","func",":","`","torch.load","`","to","save","and","load","checkpoints","respectively",",","common","for","most","use","cases",".","..","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 ``TorchCheckpointIO.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","``","TorchCheckpointIO.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 ``TorchCheckpointIO.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)
      _atomic_save(checkpoint, path)
  
file_path: pytorch-lightning/src/lightning/fabric/plugins/io/torch_io.py
imports: import logging
  import os
  from typing import Any, Callable, Dict, Optional
  from typing_extensions import override
  from lightning.fabric.plugins.io.checkpoint_io import CheckpointIO
  from lightning.fabric.utilities.cloud_io import _atomic_save, get_filesystem
  from lightning.fabric.utilities.cloud_io import _load
  from lightning.fabric.utilities.types import _PATH
label_desc: Use image node_id 1 for calling the TorchCheckpointIO obj's underlying member method code with example usage: obj.save_checkpoint(checkpoint, path, storage_options) without return types