def __resolve_ckpt_dir(self, trainer: "pl.Trainer") -> _PATH:
        """Determines model checkpoint save directory at runtime. Reference attributes from the trainer's logger to
        determine where to save checkpoints. The path for saving weights is set in this priority:

        1.  The ``ModelCheckpoint``'s ``dirpath`` if passed in
        2.  The ``Logger``'s ``log_dir`` if the trainer has loggers
        3.  The ``Trainer``'s ``default_root_dir`` if the trainer has no loggers

        The path gets extended with subdirectory "checkpoints".

        """
        if self.dirpath is not None:
            # short circuit if dirpath was passed to ModelCheckpoint
            return self.dirpath

        if len(trainer.loggers) > 0:
            if trainer.loggers[0].save_dir is not None:
                save_dir = trainer.loggers[0].save_dir
            else:
                save_dir = trainer.default_root_dir
            name = trainer.loggers[0].name
            version = trainer.loggers[0].version
            version = version if isinstance(version, str) else f"version_{version}"
            ckpt_path = os.path.join(save_dir, str(name), version, "checkpoints")
        else:
            # if no loggers, use default_root_dir
            ckpt_path = os.path.join(trainer.default_root_dir, "checkpoints")

        return ckpt_path
Determines model checkpoint save directory at runtime. Reference attributes from the trainer's logger to
        determine where to save checkpoints. The path for saving weights is set in this priority:

        1.  The ``ModelCheckpoint``'s ``dirpath`` if passed in
        2.  The ``Logger``'s ``log_dir`` if the trainer has loggers
        3.  The ``Trainer``'s ``default_root_dir`` if the trainer has no loggers

        The path gets extended with subdirectory "checkpoints".

        
src/lightning/pytorch/callbacks/model_checkpoint.py