class_docstr: Saves and loads training checkpoints.
class_docstr_tok: ["Saves","and","loads","training","checkpoints","."]
docstr: Load the last checkpoint in the training.
  
  :returns:
      - The number of the training step.
      - The checkpoint.
      - The metadata associated with the checkpoint.
docstr_tok: ["Load","the","last","checkpoint","in","the","training",".",":","returns",":","-","The","number","of","the","training","step",".","-","The","checkpoint",".","-","The","metadata","associated","with","the","checkpoint","."]
code: def load_last_checkpoint(
      self,
  ) -> Tuple[int, Dict[str, Any], Optional[Dict[str, Any]]]:
      """Load the last checkpoint in the training.
  
      :returns:
          - The number of the training step.
          - The checkpoint.
          - The metadata associated with the checkpoint.
      """
  
imports: from abc import ABC, abstractmethod
  from os import scandir
  from pathlib import Path
  from pickle import PickleError
  from shutil import rmtree
  from typing import Any, Dict, Iterator, List, Optional, Tuple, final
  import torch
  from torch.distributed.fsdp import FullyShardedDataParallel
  from torch.distributed.fsdp.api import FullStateDictConfig, StateDictType
  from torch.nn import Module
  from fairseq2.gang import Gang
  from fairseq2.models.utils.checkpoint import load_checkpoint
  from fairseq2.nn.utils.module import infer_device, reset_non_persistent_buffers, to_empty
  from fairseq2.typing import CPU, META, Device, finaloverride
label_desc: Use image node_id 3 for calling the CheckpointManager obj's underlying member method code with example usage: obj.load_last_checkpoint() without return types