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: Loads checkpoint using :func:`torch.load`, with additional handling for ``fsspec`` remote loading of files.
  
  Args:
      path: Path to checkpoint
      map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
          locations.
  
  Returns: The loaded checkpoint.
  
  Raises:
      FileNotFoundError: If ``path`` is not found by the ``fsspec`` filesystem
docstr_tok: ["Loads","checkpoint","using",":","func",":","`","torch.load","`",",","with","additional","handling","for","``","fsspec","``","remote","loading","of","files",".","Args",":","path",":","Path","to","checkpoint","map_location",":","a","function",",",":","class",":","`","torch.device","`",",","string","or","a","dict","specifying","how","to","remap","storage","locations",".","Returns",":","The","loaded","checkpoint",".","Raises",":","FileNotFoundError",":","If","``","path","``","is","not","found","by","the","``","fsspec","``","filesystem"]
code: def load_checkpoint(
      self,
      path: _PATH,
      map_location: Optional[Callable] = lambda storage, loc: storage,
  ) -> Dict[str, Any]:
      """Loads checkpoint using :func:`torch.load`, with additional handling for ``fsspec`` remote loading of files.
  
      Args:
          path: Path to checkpoint
          map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage
              locations.
  
      Returns: The loaded checkpoint.
  
      Raises:
          FileNotFoundError: If ``path`` is not found by the ``fsspec`` filesystem
  
      """
  
      # Try to read the checkpoint at `path`. If not exist, do not restore checkpoint.
      fs = get_filesystem(path)
      if not fs.exists(path):
          raise FileNotFoundError(f"Checkpoint file not found: {path}")
  
      return pl_load(path, map_location=map_location)
  
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 2 for calling the TorchCheckpointIO obj's underlying member method code with example usage: obj.load_checkpoint(path, map_location) and returns: pl_load