Normalize tutorial configs across modes

Author: Donglai Wei

connectomics/decoding/tuning/optuna_tuner.py

@@ -1771,7 +1771,7 @@ def load_and_apply_best_params(cfg, checkpoint_path=None):
     Load best parameters from Optuna tuning and apply them to test config.
 
     This function loads the best-params file generated by run_tuning()
-    and updates the test.decoding section of the config with optimized parameters.
+    and updates the merged runtime inference.decoding config with optimized parameters.
 
     Args:
         cfg: Configuration object (Config dataclass)
@@ -1815,7 +1815,7 @@ def load_and_apply_best_params(cfg, checkpoint_path=None):
     if getattr(cfg.inference, "decoding", None) is None:
         cfg.inference.decoding = []
 
-    # Find the decoding function in test.decoding that matches the tuned function
+    # Find the decoder entry in the merged runtime inference.decoding config
     decoding_function = best_params.get("decoding_function", None)
 
     if decoding_function is None: