diff --git a/onnxruntime/python/backend/backend.py b/onnxruntime/python/backend/backend.py
index 2a29ccdaa5..3c88a4eb3f 100644
--- a/onnxruntime/python/backend/backend.py
+++ b/onnxruntime/python/backend/backend.py
@@ -7,7 +7,7 @@ Implements ONNX's backend API.
 """
 from onnx.checker import check_model
 from onnx.backend.base import Backend
-from onnxruntime import InferenceSession, RunOptions, SessionOptions, get_device
+from onnxruntime import InferenceSession, SessionOptions, get_device
 from onnxruntime.backend.backend_rep import OnnxRuntimeBackendRep
 
 
@@ -91,11 +91,7 @@ class OnnxRuntimeBackend(Backend):
         :return: predictions
         """
         rep = cls.prepare(model, device, **kwargs)
-        options = RunOptions()
-        for k, v in kwargs.items():
-            if hasattr(options, k):
-                setattr(options, k, v)
-        return rep.run(inputs, options)
+        return rep.run(inputs, **kwargs)
 
     @classmethod
     def run_node(cls, node, inputs, device=None, outputs_info=None, **kwargs):
diff --git a/onnxruntime/python/backend/backend_rep.py b/onnxruntime/python/backend/backend_rep.py
index 63aa586ade..fdc2569709 100644
--- a/onnxruntime/python/backend/backend_rep.py
+++ b/onnxruntime/python/backend/backend_rep.py
@@ -6,6 +6,7 @@
 Implements ONNX's backend API.
 """
 import numpy as np
+from onnxruntime import RunOptions
 from onnx.backend.base import BackendRep
 
 
@@ -27,11 +28,17 @@ class OnnxRuntimeBackendRep(BackendRep):
         Computes the prediction.
         See :meth:`onnxruntime.InferenceSession.run`.
         """
+
+        options = RunOptions()
+        for k, v in kwargs.items():
+            if hasattr(options, k):
+                setattr(options, k, v)
+
         if isinstance(inputs, list):
             inps = {}
             for i, inp in enumerate(self._session.get_inputs()):
                 inps[inp.name] = inputs[i]
-            outs = self._session.run(None, inps)
+            outs = self._session.run(None, inps, options)
             if isinstance(outs, list):
                 return outs
             else:
@@ -42,4 +49,4 @@ class OnnxRuntimeBackendRep(BackendRep):
             if len(inp) != 1:
                 raise RuntimeError("Model expect {0} inputs".format(len(inp)))
             inps = {inp[0].name: inputs}
-            return self._session.run(None, inps)
+            return self._session.run(None, inps, options)
diff --git a/onnxruntime/test/python/onnxruntime_test_python_backend.py b/onnxruntime/test/python/onnxruntime_test_python_backend.py
index 2df6fed0cc..a2e6864f51 100644
--- a/onnxruntime/test/python/onnxruntime_test_python_backend.py
+++ b/onnxruntime/test/python/onnxruntime_test_python_backend.py
@@ -9,11 +9,12 @@ import numpy as np
 import onnxruntime as onnxrt
 from onnxruntime import datasets
 import onnxruntime.backend as backend
+from onnxruntime.backend.backend import OnnxRuntimeBackend as ort_backend
 from onnx import load
 
 
 class TestBackend(unittest.TestCase):
-    
+
     def get_name(self, name):
         if os.path.exists(name):
             return name
@@ -43,10 +44,10 @@ class TestBackend(unittest.TestCase):
         output_expected = np.array([[49.752754]], dtype=np.float32)
         np.testing.assert_allclose(output_expected, res[0], rtol=1e-05, atol=1e-08)
 
-    def testRunModelProto(self):        
+    def testRunModelProto(self):
         name = datasets.get_example("logreg_iris.onnx")
         model = load(name)
-        
+
         rep = backend.prepare(model)
         x = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
         res = rep.run(x)
@@ -57,6 +58,20 @@ class TestBackend(unittest.TestCase):
                            {0: 0.9997311234474182, 1: 1.1918064757310276e-07, 2: 0.00026869276189245284}]
         self.assertEqual(output_expected, res[1])
 
+    def testRunModelProtoApi(self):
+        name = datasets.get_example("logreg_iris.onnx")
+        model = load(name)
+
+        inputs = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
+        outputs = ort_backend.run_model(model, inputs)
+
+        output_expected = np.array([0, 0, 0], dtype=np.float32)
+        np.testing.assert_allclose(output_expected, outputs[0], rtol=1e-05, atol=1e-08)
+        output_expected = [{0: 0.950599730014801, 1: 0.027834169566631317, 2: 0.02156602405011654},
+                           {0: 0.9974970817565918, 1: 5.6299926654901356e-05, 2: 0.0024466661270707846},
+                           {0: 0.9997311234474182, 1: 1.1918064757310276e-07, 2: 0.00026869276189245284}]
+        self.assertEqual(output_expected, outputs[1])
+
 
 if __name__ == '__main__':
     unittest.main(module=__name__, buffer=True)