Simplify Functional model config construction

keras/src/models/functional.py

@@ -497,14 +497,10 @@ def process_layer(layer_data):
     # Extract config used to instantiate Functional model from the config. The
     # remaining config will be passed as keyword arguments to the Model
     # constructor.
+    # NOTE: Fields not present in config will be None in functional_config.
     functional_config = {}
-    for key in ["layers", "input_layers", "output_layers"]:
-        functional_config[key] = config.pop(key)
-    for key in ["name", "trainable"]:
-        if key in config:
-            functional_config[key] = config.pop(key)
-        else:
-            functional_config[key] = None
+    for key in ["layers", "input_layers", "output_layers", "name", "trainable"]:
+        functional_config[key] = config.pop(key, None)
 
     # First, we create all layers and enqueue nodes to be processed
     for layer_data in functional_config["layers"]: