Make keyword arguments in optimizers explicit

aesara/graph/opt.py

@@ -179,7 +179,7 @@ class SeqOptimizer(GlobalOptimizer, UserList):
         elif config.on_opt_error == "pdb":
             pdb.post_mortem(sys.exc_info()[2])
 
-    def __init__(self, *opts, **kw):
+    def __init__(self, *opts, failure_callback=None):
         """
         Parameters
         ----------
@@ -195,8 +195,7 @@ class SeqOptimizer(GlobalOptimizer, UserList):
 
         super().__init__(opts)
 
-        self.failure_callback = kw.pop("failure_callback", None)
-        assert len(kw) == 0
+        self.failure_callback = failure_callback
 
     def apply(self, fgraph):
         """Applies each `GlobalOptimizer` in ``self.data`` to `fgraph`."""
@@ -1206,6 +1205,24 @@ def local_optimizer(
     inplace: bool = False,
     requirements: Optional[Tuple[type, ...]] = (),
 ):
+    r"""A decorator used to construct `FromFunctionLocalOptimizer` instances.
+
+    Parameters
+    ----------
+    tracks :
+        The `Op` types or instances to which this optimization applies.
+    inplace :
+        A boolean indicating whether or not the optimization works in-place.
+        If ``True``, a `DestroyHandler` `Feature` is added automatically added
+        to the `FunctionGraph`\s applied to this optimization.
+    requirements :
+        `Feature` types required by this optimization.
+
+    """
+
+    if requirements is None:
+        requirements = ()
+
     def decorator(f):
         if tracks is not None:
             if len(tracks) == 0:
@@ -1257,7 +1274,7 @@ class LocalOptGroup(LocalOptimizer):
         to the outputs.
     """
 
-    def __init__(self, *optimizers, **kwargs):
+    def __init__(self, *optimizers, apply_all_opts=False, profile=False):
         if len(optimizers) == 1 and isinstance(optimizers[0], list):
             # This happen when created by LocalGroupDB.
             optimizers = tuple(optimizers[0])
@@ -1269,10 +1286,9 @@ class LocalOptGroup(LocalOptimizer):
             getattr(opt, "retains_inputs", False) for opt in optimizers
         )
 
-        self.apply_all_opts = kwargs.pop("apply_all_opts", False)
-        self.profile = kwargs.pop("profile", False)
+        self.apply_all_opts = apply_all_opts
+        self.profile = profile
         self.track_map = defaultdict(lambda: [])
-        assert len(kwargs) == 0
         if self.profile:
             self.time_opts = {}
             self.process_count = {}