diff --git a/python/tvm/relax/frontend/torch/base_fx_graph_translator.py b/python/tvm/relax/frontend/torch/base_fx_graph_translator.py
index 12b460e859ac..c1cbd3416c57 100644
--- a/python/tvm/relax/frontend/torch/base_fx_graph_translator.py
+++ b/python/tvm/relax/frontend/torch/base_fx_graph_translator.py
@@ -102,6 +102,8 @@ def _retrieve_args(self, node):
             return [self._retrieve_args(x) for x in node]
         elif isinstance(node, dict):
             return {self._retrieve_args(k): self._retrieve_args(v) for k, v in node.items()}
+        elif node is None:
+            return relax.op.null_value()
         else:
             return node
 
diff --git a/tests/python/relax/test_frontend_from_exported_program.py b/tests/python/relax/test_frontend_from_exported_program.py
index 4b0672ccc144..b35af088b530 100644
--- a/tests/python/relax/test_frontend_from_exported_program.py
+++ b/tests/python/relax/test_frontend_from_exported_program.py
@@ -6028,5 +6028,27 @@ def forward(self, x):
     np.testing.assert_allclose(pytorch_output2.numpy(), tvm_output2_np, rtol=1e-4, atol=1e-5)
 
 
+def test_tensor_none_tuple():
+    example_args = (torch.tensor([1.0, 2.0, 3.0]),)
+
+    class TensorNoneModel(Module):
+        def forward(self, x):
+            return x + 1, None
+
+    @tvm.script.ir_module
+    class Expected:
+        @R.function
+        def main(
+            x: R.Tensor((3,), dtype="float32")
+        ) -> R.Tuple(R.Tensor((3,), dtype="float32"), R.Object):
+            with R.dataflow():
+                lv: R.Tensor((3,), dtype="float32") = R.add(x, R.const(1.0, "float32"))
+                gv: R.Tuple(R.Tensor((3,), dtype="float32"), R.Object) = (lv, R.null_value())
+                R.output(gv)
+            return gv
+
+    verify_model(TensorNoneModel(), example_args, {}, Expected)
+
+
 if __name__ == "__main__":
     tvm.testing.main()