Corrected the ReduceSumSqure op to match the reference (#6121)

### Description
This PR aligns the op implementation for `ReduceSumSquare18` when `axes`
are not specified and `noop_with_empty_axes != 0` with that of the
reference.

### Motivation and Context
Current implementation of `ReduceSumSquare18` when axes are empty and
`noop_with_empty_axes != 1` squares the input data and then returns it,
when according to the reference it should just return the input data
with no changes.

This addresses issue #6103

Signed-off-by: Aman K Shihab <amanshihab276@gmail.com>

onnx/reference/ops/op_reduce_sum_square.py

@@ -21,7 +21,7 @@ def _run(self, data, axes=None, keepdims=None):  # type: ignore
 class ReduceSumSquare_18(OpRunReduceNumpy):
     def _run(self, data, axes=None, keepdims=1, noop_with_empty_axes=0):  # type: ignore
         if self.is_axes_empty(axes) and noop_with_empty_axes != 0:  # type: ignore
-            return (np.square(data),)
+            return (data,)
 
         axes = self.handle_axes(axes)
         keepdims = keepdims != 0  # type: ignore

onnx/test/reference_evaluator_test.py

@@ -738,7 +738,7 @@ def test_reduce_sum_square_18_empty_axes_noop(self):
         x = np.arange(60).reshape((3, 4, 5)).astype(np.float32)
         sess = ReferenceEvaluator(onnx_model)
         got = sess.run(None, {"X": x})[0]
-        assert_allclose(x * x, got)
+        assert_allclose(x, got)
 
     def test_greater(self):
         X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])