diff --git a/onnxscript/function_libs/torch_lib/graph_building.py b/onnxscript/function_libs/torch_lib/graph_building.py
index aa318d523..057e1a922 100644
--- a/onnxscript/function_libs/torch_lib/graph_building.py
+++ b/onnxscript/function_libs/torch_lib/graph_building.py
@@ -731,6 +731,21 @@ def _add_torchscript_op_call(
             tensor.name = _rename_intermediate_value(tensor.name)
         return tensors
 
+    @runtime_typing.checked
+    def fetch_opset_imports(self) -> Dict[str, int]:
+        # TODO: All local function domain versions are hardcoded as 1.
+        # TODO: Improve the efficiency of this approach.
+        #   This function recursively fetches opset imports from subgraphs.
+        opset_imports: Dict[str, int] = {}
+        for subgraph in self._sub_torch_script_graphs.values():
+            opset_imports.update(subgraph.fetch_opset_imports())
+            if subgraph.domain_name is not None:
+                opset_imports[subgraph.domain_name] = 1
+        for function in self._function_store.values():
+            opset_imports.update(function.function_ir.get_opset_import())
+            opset_imports[function.function_ir.domain] = 1
+        return opset_imports
+
     @runtime_typing.checked
     def fetch_function_proto_dict(
         self, opset_version: int
@@ -925,6 +940,18 @@ def to_function_proto(self, opset_version: int, function_name: str) -> onnx.Func
 
         onnx_model = onnx.load_from_string(proto)
 
+        unique_custom_domains = self.fetch_opset_imports()
+
+        for opset in onnx_model.opset_import:
+            unique_custom_domains[opset.domain] = opset.version
+        del onnx_model.opset_import[:]
+        onnx_model.opset_import.extend(
+            [
+                onnx.helper.make_opsetid(domain, version)
+                for domain, version in unique_custom_domains.items()
+            ]
+        )
+
         # Dissect the model proto and transform to function proto.
         domain = self.domain_name
         if domain is None:
@@ -947,11 +974,7 @@ def to_model_proto(
         function_proto_dict: Mapping[
             Tuple[str, str], onnx.FunctionProto
         ] = self.fetch_function_proto_dict(opset_version)
-        unique_custom_domains: Dict[str, int] = {}
-
-        for function_proto in function_proto_dict.values():
-            # TODO(BowenBao): All local function domain versions are hardcoded as 1.
-            unique_custom_domains[function_proto.domain] = 1
+        unique_custom_domains = self.fetch_opset_imports()
 
         initializers_size = sum(
             _tensor_rawdata_size(tensor) for tensor in self.initializers.values()
@@ -1017,20 +1040,13 @@ def to_model_proto(
         # export opset_imports for nested functions, since it does not have access to
         # them. We manually add them back and merge with existing opset_imports in the
         # model proto.
-        while len(onnx_model.opset_import) > 0:
-            opsetid = onnx_model.opset_import.pop()
-            unique_custom_domains[opsetid.domain] = opsetid.version
+        for opset in onnx_model.opset_import:
+            unique_custom_domains[opset.domain] = opset.version
+        del onnx_model.opset_import[:]
         onnx_model.opset_import.extend(
             [
                 onnx.helper.make_opsetid(domain, version)
                 for domain, version in unique_custom_domains.items()
             ]
         )
-        # Include the library shared opset domain
-        # TODO: Remove after https://github.com/microsoft/onnxscript/issues/834 is fixed
-        onnx_model.opset_import.append(
-            onnx.helper.make_opsetid(
-                common_ops.common_opset.domain, common_ops.common_opset.version
-            )
-        )
         return onnx_model