def test_shadow_property_smart(self):
        x = numpy.linspace(0, 1)
        d = DataSmart(x=x)
        self.assertEqual(numpy.all(d.x == x), True)
        self.assertEqual(numpy.all(d._x == x), True)
        self.assertEqual(numpy.all(d._test == x), True)
        old = x.copy()
        x *= 2
        d.x = x
        self.assertEqual(numpy.all(d.x == x), True)
        self.assertEqual(numpy.all(d._x == x), True)
        # Notifier shouldn't be called.
        self.assertEqual(numpy.all(d._test == old), True)
Apply the forward pass transformation to the input data.  The input data is a list of
dataset/ETHPy150Open enthought/mayavi/mayavi/tests/test_mayavi_traits.py/TestShadowProperty.test_shadow_property_smart--crossover with--dataset/ETHPy150Open NervanaSystems/neon/neon/layers/recurrent.py/GRU.fprop