`memory_demanding` for TF based transformers

There are some situations where you have SampleSets with more than 1000 samples and we can't just TF forward them in one shot without OOM. In this MR I introduce the argument memory_demanding, where, once it's set to true, will `forward one sample at a time.

bob/bio/face/config/baseline/facenet_sanderberg.py

@@ -7,10 +7,14 @@ from bob.bio.base.pipelines.vanilla_biometrics import (
     VanillaBiometricsPipeline,
 )
 
-
+memory_demanding = False
 if "database" in locals():
     annotation_type = database.annotation_type
     fixed_positions = database.fixed_positions
+    memory_demanding = (
+        database.memory_demanding if hasattr(database, "memory_demanding") else False
+    )
+
 else:
     annotation_type = None
     fixed_positions = None
@@ -19,7 +23,9 @@ else:
 def load(annotation_type, fixed_positions=None):
 
     transformer = embedding_transformer_160x160(
-        FaceNetSanderberg_20170512_110547(), annotation_type, fixed_positions
+        FaceNetSanderberg_20170512_110547(memory_demanding=memory_demanding),
+        annotation_type,
+        fixed_positions,
     )
     algorithm = Distance()