diff --git a/test/onnx/test_utility_funs.py b/test/onnx/test_utility_funs.py
index d7bef668118..dca45fc5c31 100644
--- a/test/onnx/test_utility_funs.py
+++ b/test/onnx/test_utility_funs.py
@@ -752,6 +752,24 @@ class TestUtilityFuns_opset9(_BaseTestCase):
         self.assertIn("NWithOverloads.1", func_names)
         self.assertIn("NWithOverloads.2", func_names)
 
+    @skipIfUnsupportedMinOpsetVersion(15)
+    def test_local_function_infer_scopes(self):
+        class M(torch.nn.Module):
+            def forward(self, x):
+                # Concatenation of scalars inserts unscoped tensors in IR graph.
+                new_tensor_shape = x.size()[:-1] + (1, 1, -1)
+                tensor = x.view(*new_tensor_shape)
+                return tensor
+
+        x = torch.randn(4, 5)
+        f = io.BytesIO()
+        torch.onnx.export(M(), (x,), f, export_modules_as_functions=True,
+                          opset_version=self.opset_version, do_constant_folding=False)
+
+        onnx_model = onnx.load(io.BytesIO(f.getvalue()))
+        funcs = onnx_model.functions
+        self.assertIn("M", [f.name for f in funcs])
+
     def test_aten_fallthrough(self):
         # Test aten export of op with no symbolic
         class Module(torch.nn.Module):
@@ -1222,5 +1240,9 @@ class TestUtilityFuns_opset14(TestUtilityFuns_opset9):
     opset_version = 14
 
 
+class TestUtilityFuns_opset15(TestUtilityFuns_opset9):
+    opset_version = 15
+
+
 if __name__ == "__main__":
     run_tests()
diff --git a/torch/csrc/jit/passes/onnx/function_extraction.cpp b/torch/csrc/jit/passes/onnx/function_extraction.cpp
index 5a0f8592f3b..1840d96fd13 100644
--- a/torch/csrc/jit/passes/onnx/function_extraction.cpp
+++ b/torch/csrc/jit/passes/onnx/function_extraction.cpp
@@ -353,6 +353,13 @@ c10::optional<ScopePtr> FunctionExtractor::InferScope(Node* n) {
   }
   for (auto output : n->outputs()) {
     for (auto use : output->uses()) {
+      if (!IsValidScope(use.user->scope())) {
+        auto inferred_output_scope = InferScope(use.user);
+        if (inferred_output_scope.has_value() &&
+            IsValidScope(inferred_output_scope.value())) {
+          use.user->setScope(inferred_output_scope.value());
+        }
+      }
       output_scopes.emplace_back(use.user->scope());
     }
   }
diff --git a/torch/csrc/jit/passes/onnx/helper.cpp b/torch/csrc/jit/passes/onnx/helper.cpp
index 83206935048..f76b606c181 100644
--- a/torch/csrc/jit/passes/onnx/helper.cpp
+++ b/torch/csrc/jit/passes/onnx/helper.cpp
@@ -189,6 +189,7 @@ Node* transformToONNXConcatNode(
 
     Node* unsqueezed_node =
         createONNXUnsqueeze(g, new_node, new_input, 0, opset_version);
+    unsqueezed_node->copyMetadata(lc_node);
     unsqueezed.emplace_back(unsqueezed_node->output());
   }
 
diff --git a/torch/onnx/__init__.py b/torch/onnx/__init__.py
index 31bffdabca1..2a049f37e35 100644
--- a/torch/onnx/__init__.py
+++ b/torch/onnx/__init__.py
@@ -9,8 +9,6 @@ ONNX_ARCHIVE_MODEL_PROTO_NAME = "__MODEL_PROTO"
 
 producer_name = "pytorch"
 producer_version = _C._onnx.PRODUCER_VERSION
-constant_folding_opset_versions = [9, 10, 11, 12, 13, 14]
-
 
 class ExportTypes:
     r""""Specifies how the ONNX model is stored."""
diff --git a/torch/onnx/symbolic_helper.py b/torch/onnx/symbolic_helper.py
index 3d62dc2df3c..d6746967d76 100644
--- a/torch/onnx/symbolic_helper.py
+++ b/torch/onnx/symbolic_helper.py
@@ -839,6 +839,7 @@ _default_onnx_opset_version = 9
 _onnx_main_opset = 15
 _onnx_stable_opsets = [7, 8, 9, 10, 11, 12, 13, 14]
 _export_onnx_opset_version = _default_onnx_opset_version
+_constant_folding_opset_versions = list(range(9, _onnx_main_opset + 1))
 
 
 def _set_opset_version(opset_version):
diff --git a/torch/onnx/utils.py b/torch/onnx/utils.py
index ad960a4b3c1..50023313ee7 100644
--- a/torch/onnx/utils.py
+++ b/torch/onnx/utils.py
@@ -532,7 +532,8 @@ def _model_to_graph(model, args, verbose=False,
     if training is None or training == TrainingMode.EVAL:
         params_dict = torch._C._jit_pass_onnx_eval_peephole(graph, params_dict)
 
-    if do_constant_folding and _export_onnx_opset_version in torch.onnx.constant_folding_opset_versions:
+    from torch.onnx.symbolic_helper import _constant_folding_opset_versions
+    if do_constant_folding and _export_onnx_opset_version in _constant_folding_opset_versions:
         params_dict = torch._C._jit_pass_onnx_constant_fold(graph, params_dict,
                                                             _export_onnx_opset_version)
         torch._C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)