def METHOD_NAME(self, scaler_arg=('standard', StandardScaler())):
    scaler_name, scaler = scaler_arg
    original = numpy.ones([2, 2], dtype='float32')
    # Use Scipy to generate the expected results
    expected =scaler.fit_transform(original.reshape(-1, 1))
    # Add singleton dimensions to trainsform the input vector into a tensor
    original = numpy.expand_dims(original, axis=[0, 1])
    result = TensorNormalize(scaler=scaler_name, axis=-1)(original)
    self.assertAlmostEqualVector(result.flatten(), expected.flatten(), 1e-6)