

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