class_docstr: Saves and loads training checkpoints.
class_docstr_tok: ["Saves","and","loads","training","checkpoints","."]
docstr: Return the number of the training step associated with the last
  checkpoint.
  
  :param with_model:
      If ``True``, only considers checkpoints with a consolidated model.
docstr_tok: ["Return","the","number","of","the","training","step","associated","with","the","last","checkpoint",".",":","param","with_model",":","If","``","True","``",",","only","considers","checkpoints","with","a","consolidated","model","."]
code: def get_last_step_number(
      self, *, with_model: bool = False
  ) -> Optional[int]:
      """Return the number of the training step associated with the last
      checkpoint.
  
      :param with_model:
          If ``True``, only considers checkpoints with a consolidated model.
      """
  
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 11 for calling the CheckpointManager obj's underlying member method code with example usage: obj.get_last_step_number() without return types