diff --git a/test/test_fx_experimental.py b/test/test_fx_experimental.py
index 18c12a99f6e1..c587b6f23633 100644
--- a/test/test_fx_experimental.py
+++ b/test/test_fx_experimental.py
@@ -1678,6 +1678,13 @@ class TestModule(torch.nn.Module):
         )
         self.assertEqual(norm_args_and_kwargs.args, tuple())
 
+    def test_normalize_args_op_overload(self):
+        for target in [torch.ops.aten.resize_as_.default, torch.ops.aten.resize_as_]:
+            inp1 = torch.rand([1])
+            inp2 = torch.rand([4])
+            args, kwargs = normalize_function(target, (inp1,), {"the_template": inp2}, normalize_to_only_use_kwargs=True)
+            self.assertIs(kwargs["input"], inp1)
+            self.assertIs(kwargs["the_template"], inp2)
 
 instantiate_device_type_tests(TestNormalizeOperators, globals())
 
diff --git a/torch/fx/operator_schemas.py b/torch/fx/operator_schemas.py
index 0b72afaca2ed..9848baa04c10 100644
--- a/torch/fx/operator_schemas.py
+++ b/torch/fx/operator_schemas.py
@@ -135,20 +135,22 @@ def get_signature_for_torch_op(op : Callable, return_schemas : bool = False):
             return_schemas=True, returns a tuple containing the optional Python signatures
             and the optional TorchScript Function signature
     """
-    if isinstance(op, OpOverloadPacket) or isinstance(op, OpOverload):
-        op = op.op
-    override = _manual_overrides.get(op)
-    if override:
-        return (override, None) if return_schemas else None
+    if isinstance(op, OpOverload):
+        schemas = [op._schema]
+    elif isinstance(op, OpOverloadPacket):
+        schemas = [getattr(op, overload)._schema for overload in op.overloads()]
+    else:
+        override = _manual_overrides.get(op)
+        if override:
+            return (override, None) if return_schemas else None
 
-    aten_fn = torch.jit._builtins._find_builtin(op)
+        aten_fn = torch.jit._builtins._find_builtin(op)
 
-    if aten_fn is None:
-        return (None, None) if return_schemas else None
+        if aten_fn is None:
+            return (None, None) if return_schemas else None
+        schemas = torch._C._jit_get_schemas_for_operator(aten_fn)
 
-    schemas = torch._C._jit_get_schemas_for_operator(aten_fn)
     signatures = [_torchscript_schema_to_signature(schema) for schema in schemas]
-
     return (signatures, schemas) if return_schemas else signatures
 
 @compatibility(is_backward_compatible=False)
@@ -257,9 +259,9 @@ def normalize_function(
     if kwargs is None:
         kwargs = {}
     new_args_and_kwargs = None
-    if isinstance(target, OpOverloadPacket) or isinstance(target, OpOverload):
-        target = target.op
-    if not isinstance(target, types.BuiltinFunctionType):
+    if not isinstance(target, types.BuiltinFunctionType) and not (
+        isinstance(target, OpOverloadPacket) or isinstance(target, OpOverload)
+    ):
         target_for_analysis = target
         if target in boolean_dispatched:
             # HACK: `boolean_dispatch` as used in `torch.nn.functional` makes it so that we have