

def grad(self, inp, grads):
    (x,) = inp
    (gz,) = grads
    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 * psi(x))]

dataset/ETHPy150Open rizar/attention-lvcsr/libs/Theano/theano/scalar/basic_scipy.py GammaLn.grad/original