class_docstr: Class that customizes how checkpoints are saved. Saves either the entire model or only parameters that have been explicitly updated during training. The latter reduces memory footprint substantially when training very large models with parameter-efficient finetuning methods.
  Class is based on plugins.TorchCheckpointIO.
docstr: Load checkpoint from a path when resuming or loading ckpt for test/validate/predict stages.
  
  Parameters
  ----------
  path
      Path to checkpoint
  map_location
      a function, torch.device, string or a dict specifying how to remap storage locations.
docstr_tok: ["Load","checkpoint","from","a","path","when","resuming","or","loading","ckpt","for","test\/validate\/predict","stages",".","Parameters","--","--","--","--","--","path","Path","to","checkpoint","map_location","a","function",",","torch.device",",","string","or","a","dict","specifying","how","to","remap","storage","locations","."]
code: def load_checkpoint(
      self, path, map_location: Optional[Any] = None
  ) -> Dict[str, Any]:
      """
      Load checkpoint from a path when resuming or loading ckpt for test/validate/predict stages.
  
      Parameters
      ----------
      path
          Path to checkpoint
      map_location
          a function, torch.device, string or a dict specifying how to remap storage locations.
      """
  
      fs = get_filesystem(path)
      if not fs.exists(path):
          raise FileNotFoundError(
              f"Checkpoint at {path} not found. Aborting training."
          )
  
      return pl_load(path, map_location=map_location)
  
imports: import logging
  import os
  import re
  import shutil
  from typing import Any, Dict, List, Optional, Tuple, Union
  import lightning.pytorch
  import torch
  from lightning.pytorch.strategies import DeepSpeedStrategy
  from lightning.pytorch.utilities.rank_zero import rank_zero_warn
  from .cloud_io import _atomic_save
  from .cloud_io import _load
  from .cloud_io import get_filesystem
label_desc: Use image node_id 3 for calling the AutoMMModelCheckpointIO obj's underlying member method code with example usage: obj.load_checkpoint(path, map_location) and returns: pl_load