remuve commented code

aesara/graph/opt.py

@@ -1069,63 +1069,6 @@ def recursive_merge(var):
     return [recursive_merge(v) for v in variables]
 
 
-# class LocalOptimizer(abc.ABC):
-#    """A node-based optimizer."""
-#
-#    def __hash__(self):
-#        if not hasattr(self, "_optimizer_idx"):
-#            self._optimizer_idx = _optimizer_idx[0]
-#            _optimizer_idx[0] += 1
-#        return self._optimizer_idx
-#
-#    def tracks(self):
-#        """
-#        Return the list of op classes that this opt applies to.
-#
-#        Return None to apply to all nodes.
-#
-#        """
-#       return None
-#
-#    @abc.abstractmethod
-#    def transform(self, fgraph, node, *args, **kwargs):
-#        """
-#        Transform a subgraph whose output is `node`.
-#
-#        Subclasses should implement this function so that it returns one of two
-#        kinds of things:
-#
-#        - False to indicate that no optimization can be applied to this `node`;
-#          or
-#        - <list of variables> to use in place of `node`'s outputs in the
-#          greater graph.
-#        - dict(old variables -> new variables). A dictionary that map
-#          from old variables to new variables to replace.
-#
-#        Parameters
-#        ----------
-#        node : an Apply instance
-#
-#        """
-#
-#        raise NotImplementedError()
-#
-#    def add_requirements(self, fgraph):
-#        """
-#        If this local optimization wants to add some requirements to the
-#        fgraph, this is the place to do it.
-#
-#        """
-#
-#    def print_summary(self, stream=sys.stdout, level=0, depth=-1):
-#        print(f"{' ' * level}{self.__class__.__name__} id={id(self)}", file=stream)
-#
-#    def __str__(self):
-#        if hasattr(self, "name"):
-#            return f"{type(self).__name__}[{self.name}]"
-#    return repr(self)
-
-
 class LocalMetaOptimizer(LocalOptimizer):
     r"""
     Base class for meta-optimizers that try a set of `LocalOptimizer`\s

aesara/sparse/type.py

@@ -150,13 +150,6 @@ def may_share_memory(a, b):
     def make_variable(self, name=None):
         return self.Variable(self, name=name)
 
-    # def __eq__(self, other):
-    #    return (
-    #        super().__eq__(other)
-    #        and type(self) == type(other)
-    #        and other.format == self.format
-    #    )
-
     def __hash__(self):
         return super().__hash__() ^ hash(self.format)
 
@@ -222,16 +215,12 @@ def __eq__(self, other):
         return other.dtype == self.dtype and other.format == self.format
 
     def is_super(self, otype):
-        # if (
-        #    isinstance(otype, SparseType)
-        #    and otype.dtype == self.dtype
-        #    and otype.ndim == self.ndim
-        #    and self.format == otype.format
-        #    and otype.broadcastable == self.broadcastable
-        # ):
-        #    return True
-        # return False
-        if self == otype:
+        if (
+            isinstance(otype, type(self))
+            and otype.dtype == self.dtype
+            and otype.ndim == self.ndim
+            and self.format == otype.format
+        ):
             return True
         return False