BVh @dZdZdZdZdad dZdZGddeZy ) zCheckpoint compatibility functions with SaveableObject. Compatibility methods to ensure that checkpoints are saved with the same metadata attributes before/after the SaveableObject deprecation. _LEGACY_SAVEABLE_NAMEcfd}|S)aDDecorator to set the local name to use in the Checkpoint. Needed for migrating certain Trackables (see next paragraph) from the legacy `_gather_saveables_for_checkpoint` to the new `_serialize_to_tensors` function. This decorator should be used if the SaveableObject generates tensors with different names from the name that is passed to the factory. Example migration: *Before* ``` class MyTrackable(Trackable): def _gather_saveables_for_checkpoint(self): return {"key": _MySaveable} class _MySaveable(SaveableObject): def __init__(self, name): specs = [ SaveSpec(tensor1, "", name + "-1") SaveSpec(tensor2, "", name + "-2") ] super().__init__(None, specs, name) ``` *After* ``` @legacy_saveable_name("key") class MyTrackable(Trackable): def _serialize_to_tensors(self): return {"key-1": tensor1, "key-2": tensor2} ``` Args: name: String name of the SaveableObject factory (the key returned in the `_gather_saveables_for_checkpoint` function) Returns: A decorator. c*t|t|SN)setattrr) cls_or_objnames \/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/checkpoint/saveable_compat.py decoratorz'legacy_saveable_name..decoratorEs J-t4 )rr s` r legacy_saveable_namer sZ r c$t|tdSr)getattrr)rs r get_saveable_namerKs 2D 99r Fc|ay)acForces checkpoint to use the new implementation. The new checkpoint implementation is changing the saved metadata slightly, and therefore may break forward compatibility in newly saved checkpoints. This means: - Previous versions of TensorFlow may not be able to load new checkpoints. - Backwards compatibility is unchanged: Old checkpoints can still be loaded. TensorFlow guarantees 3 weeks of forward compatibility, so this flag will be removed in the future weeks, after which checkpoint conversion will happen by default. **What happens when this flag is enabled?** The checkpoint will be saved with different metadata, meaning that previous versions of TensorFlow (<=2.10) will not be able to load this checkpoint. Args: value: Boolean value, whether or not to force checkpoint conversion to the new implementation. N_FORCE_CHECKPOINT_CONVERSION)values r force_checkpoint_conversionrRs 2"'r ctSrrr r r #force_checkpoint_conversion_enabledrns %%r c eZdZy)CheckpointConversionErrorN)__name__ __module__ __qualname__r r r rrrsr rN)T) __doc__rr rrrr Exceptionrr r r rs; 00f: %'8& r