def grad(self, inputs, gout):
        (x,) = inputs
        (gz,) = gout
        if x.type in complex_types:
            raise NotImplementedError()
        if self(x).type in discrete_types:
            if x.type in discrete_types:
                return [x.zeros_like(dtype=theano.config.floatX)]
            else:
                return [x.zeros_like()]

        return gz * gamma(x) * psi(x),
dataset/ETHPy150Open rizar/attention-lvcsr/libs/Theano/theano/scalar/basic_scipy.py/Gamma.grad/original