Auto tracking for Python lists assigned to attributes of Model/Checkpointable

Conceptually lists just get replaced with a list-like wrapper. A shallow copy is maintained for error checking (since appends to it aren't monitored, we can't do restore-on-create for variables unless it's being modified through the wrapper).

There are lots of other details. I gave up on generalizing our isinstance(obj, (list, tuple)) checks and just subclassed list. Behaving like a list means the type should be unhashable, which requires some workarounds when we're collecting objects (object-identity collections, and object-identity versions of weak reference containers).

Adds a decorator for exempting whole methods from automatic dependency tracking so we don't need to track down every last self.inputs = [] statement to avoid polluting dependencies.

There's a TODO for tuples and dictionaries.

PiperOrigin-RevId: 202703271

1 files changed, 1 insertions, 1 deletions

diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py
index f3b788f931..e037925961 100644
--- a/tensorflow/python/client/session.py
+++ b/tensorflow/python/client/session.py
@@ -361,7 +361,7 @@ class _ListFetchMapper(_FetchMapper):
     for m, vi in zip(self._mappers, self._value_indices):
       results.append(m.build_results([values[j] for j in vi]))
     # Return a value of the original type of the fetches.
-    if self._fetch_type == list:
+    if issubclass(self._fetch_type, list):
       return results
     elif self._fetch_type == tuple:
       return tuple(results)