class_docstr: A model and optimizer checkpoint manager.
class_docstr_tok: ["A","model","and","optimizer","checkpoint","manager","."]
code: def get_best_checkpoint_state_for_inference(
      self, device: torch.device
  ) -> Tuple[Mapping[str, Any], None]:
      save_path = os.path.join(self.directory, f"{BEST}.ckpt")
      try:
          return self.checkpoint.get_state_for_inference(
              save_path, device
          )
      except Exception:
          # This exception may be hit if the best checkpoint does not exist. This can happen if the model runs into
          # NaN loss because of NaN or inf values in the weights before the first checkpoint is saved. In this case,
          logger.error(
              f"Could not load best checkpoint state from {save_path}. Best checkpoint may not exist."
          )
          return None
  
imports: import errno
  import logging
  import os
  import re
  import shutil
  import signal
  import tempfile
  import uuid
  from abc import ABC, abstractmethod
  from glob import glob
  from typing import Any, Dict, Mapping, Optional, Tuple, TYPE_CHECKING
  import torch
  from torch.optim import Optimizer
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.modules.lr_scheduler import LRScheduler
label_desc: Use image node_id 6 for calling the CheckpointManager obj's underlying member method code with example usage: obj.get_best_checkpoint_state_for_inference(device) and returns: self, None