diff --git a/onnxruntime/python/tools/symbolic_shape_infer.py b/onnxruntime/python/tools/symbolic_shape_infer.py
index 869a048511..2c50b4a6b0 100755
--- a/onnxruntime/python/tools/symbolic_shape_infer.py
+++ b/onnxruntime/python/tools/symbolic_shape_infer.py
@@ -1331,6 +1331,7 @@ class SymbolicShapeInference:
         vi = self.known_vi_[node.output[0]]
         elem_type = self.known_vi_[node.input[0]].type.tensor_type.elem_type
         vi.type.tensor_type.elem_type = elem_type
+        vi.type.tensor_type.shape.CopyFrom(onnx.TensorShapeProto())
 
         if len(node.output) > 1:
             data_shape = self._get_shape(node, 0)
diff --git a/onnxruntime/test/python/onnxruntime_test_python_symbolic_shape_infer.py b/onnxruntime/test/python/onnxruntime_test_python_symbolic_shape_infer.py
index 6ba3a091ac..dd4ed8b437 100644
--- a/onnxruntime/test/python/onnxruntime_test_python_symbolic_shape_infer.py
+++ b/onnxruntime/test/python/onnxruntime_test_python_symbolic_shape_infer.py
@@ -134,6 +134,32 @@ class TestSymbolicShapeInferenceForOperators(unittest.TestCase):
         ]
         self._check_shapes(graph, inferred.graph, expected_shapes)
 
+    def test_softmax_cross_entropy_loss(self):
+        hidden_size = 1024
+
+        nodes = [
+            helper.make_node("SoftmaxCrossEntropyLoss",
+                             inputs=["logits", "labels"],
+                             outputs=["loss"]),
+        ]
+
+        inputs = [
+            helper.make_tensor_value_info('logits', TensorProto.FLOAT, ['b', 's', hidden_size]),
+            helper.make_tensor_value_info('labels', TensorProto.INT32, ['b', 's']),
+        ]
+
+        outputs = [
+            helper.make_tensor_value_info('loss', TensorProto.FLOAT, None),
+        ]
+
+        graph = helper.make_graph(nodes, "SoftmaxCrossEntropyLoss_Test", inputs, outputs, [])
+        model = helper.make_model(graph)
+
+        inferred = SymbolicShapeInference.infer_shapes(model, auto_merge=True)
+        expected_shapes = [
+            helper.make_tensor_value_info('loss', TensorProto.FLOAT, [])
+        ]
+        self._check_shapes(graph, inferred.graph, expected_shapes)
 
 class TestSymbolicShapeInferenceForSlice(unittest.TestCase):
     def check_slice_of_concat(self, input_dims, start, end, step, expected_output_dim):