[CheckpointStatus] - add checkpoint status handler; Model.train() accepts status param

status.py: add CheckpointStatus(save_fn, update_interval) — prints progress
and calls save_fn at every report interval to persist model state mid-training.
model.py: Model.train() now accepts an optional Status instance; defaults to
plain Status() if none provided.
test_faces_sub_image.py: use CheckpointStatus(model.save) during training.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/rbm/model.py

@@ -20,12 +20,12 @@ class Model(ABC):
 				obj_list.append(value)
 		return obj_list
 
-	def train(self, batch: Mat):
+	def train(self, batch: Mat, status: Status = None):
 		entities = self.objects(Entity)
 		_batch = np.array(batch)   # np.array() converts numpy→CuPy; np.copy() does not
 		for entity in entities:
 			if entity.enable_training:
-				train(entity, _batch, Status())
+				train(entity, _batch, status if status is not None else Status())
 			_batch = entity.forward(_batch)
 
 	def forward(self, x: Mat) -> Mat: