"""Auto-generated stub for module: checkpoint."""
from typing import Any, List

# Constants
logger: Any

# Functions
def load_checkpoint(path: str | Any) -> dict[str, Any]:
    """
    Load a checkpoint from disk.
    
        Args:
            path: Path to the ``.pth.tar`` checkpoint file.
            map_location: Device to map tensors to (default ``"cpu"``
                for safe cross-device loading).
    
        Returns:
            The checkpoint state dict.
    
        Raises:
            FileNotFoundError: If *path* does not exist.
    """
    ...
def save_checkpoint(state: dict[str, Any], model: Any.Any | Any) -> list[Any]:
    """
    Save a training checkpoint to disk.
    
        Writes ``state`` (typically epoch, state_dict, optimizer, scheduler)
        to *checkpoint_dir/filename*.  When *is_best* is ``True``, also:
    
        * Copies the checkpoint to ``model_best.pth.tar``.
        * Saves the raw model to ``model_best.pt`` (handles
          ``DataParallel`` unwrapping automatically).
    
        Args:
            state: Serialisable dict — must include at minimum ``epoch``
                and ``state_dict``.
            model: The PyTorch model (or ``DataParallel``-wrapped model).
                Used only when *is_best* to create the ``.pt`` snapshot.
            is_best: Whether the current checkpoint is the best so far.
            checkpoint_dir: Directory where files are written.
            filename: Name of the periodic checkpoint file.
    
        Returns:
            List of :class:`Path` objects for every file written.
    """
    ...
