diff --git a/test/stablehlo/test_xla_export_interpreter.py b/test/stablehlo/test_xla_export_interpreter.py
new file mode 100644
index 00000000000..88d8af410f2
--- /dev/null
+++ b/test/stablehlo/test_xla_export_interpreter.py
@@ -0,0 +1,33 @@
+import re
+import sys
+import unittest
+
+import torch
+import torch_xla
+import torch_xla.core.xla_model as xm
+from torch_xla.stablehlo import exported_program_to_stablehlo
+
+device = xm.xla_device()
+
+
+class XLAExportInterpreterTest(unittest.TestCase):
+
+  def test_constant_wrapping(self):
+
+    class M(torch.nn.Module):
+
+      def forward(self, x):
+        return 1.0 - x
+
+    ep = torch.export.export(M(), (torch.rand(2, 3),))
+    ep = ep.run_decompositions()
+    shlo_module = exported_program_to_stablehlo(ep)
+    shlo_text = shlo_module.get_stablehlo_text()
+    self.assertTrue(
+        re.search(r'stablehlo.constant.* : tensor<2x3xf32>', shlo_text)
+        is not None)
+
+
+if __name__ == '__main__':
+  test = unittest.main()
+  sys.exit(0 if test.result.wasSuccessful() else 1)
diff --git a/torch_xla/stablehlo.py b/torch_xla/stablehlo.py
index 09e21514b8f..861e587ae15 100644
--- a/torch_xla/stablehlo.py
+++ b/torch_xla/stablehlo.py
@@ -233,6 +233,22 @@ def call_function(self, target, args: Tuple, kwargs: Dict) -> Any:
     new_kwargs = dict(kwargs)
     if 'device' in kwargs:
       new_kwargs['device'] = self._device
+    # Note: Use `_disable_current_modes` to alwasys create constant tensor.
+    # Under `fake_tensor_mode` a fake tensor will be created. This is not a
+    # use case for XLAExportInterpreter right now, adding to be future-proof.
+    with torch.utils._python_dispatch._disable_current_modes():
+      # If the op spec expects a `Tensor` input, we wrap the python primitive
+      # type to a torch.tensor. The dtype for float respects
+      # `torch.get_default_dtype`. Without this wrapping, the python float
+      # will be wrapped before it enters dispatcher, and it doesn't respect
+      # the global default dtype.
+      args_and_specs = tuple(zip(args, target._schema.arguments))
+      args = tuple(
+          map(
+              lambda arg_spec: torch.tensor(arg_spec[0])
+              if isinstance(arg_spec[0], (float, int, bool)) and type(arg_spec[
+                  1].type) == torch.TensorType else arg_spec[0],
+              args_and_specss))
     return super().call_function(target, args, new_kwargs)
 
   def run_node(self, n) -> Any: