def save_checkpoint(
        self,
        path: _PATH,
        state: dict[str, Union[Module, Optimizer, Any]],
        storage_options: Optional[Any] = None,
        filter: Optional[dict[str, Callable[[str, Any], bool]]] = None,
    ) -> None:
        """Save model, optimizer, and other state as a checkpoint file.

        Args:
            path: A path to where the file(s) should be saved
            state: A dictionary with contents to be saved. If the dict contains modules or optimizers, their
                state-dict will be retrieved and converted automatically.
            storage_options: Additional options for the ``CheckpointIO`` plugin
            filter: An optional dictionary of the same format as ``state`` mapping keys to callables that return a
                boolean indicating whether the given parameter should be saved (``True``) or filtered out (``False``).

        """
        import torch_xla.core.xla_model as xm

        # sync any pending lazy tensors on all ranks before saving to prevent potential collective hangs
        xm.mark_step()
        # save on global rank zero only
        super().save_checkpoint(path, state, storage_options=storage_options, filter=filter)
Save model, optimizer, and other state as a checkpoint file.

        Args:
            path: A path to where the file(s) should be saved
            state: A dictionary with contents to be saved. If the dict contains modules or optimizers, their
                state-dict will be retrieved and converted automatically.
            storage_options: Additional options for the ``CheckpointIO`` plugin
            filter: An optional dictionary of the same format as ``state`` mapping keys to callables that return a
                boolean indicating whether the given parameter should be saved (``True``) or filtered out (``False``).

        
src/lightning/fabric/strategies/xla.py