@neural_computation
    def compute_new_state(self, step_inputs):
        xh_t, h_tm1 = map(step_inputs.get, ["xh_t", "state"])
        if not xh_t:
            xh_t = 0

        h_t = self.activate(xh_t - T.dot(h_tm1, self.W_h) + self.b_h)

        return {"state": h_t}
dataset/ETHPy150Open zomux/deepy/deepy/layers/recurrent.py/RNN.compute_new_state/operator `+` -> `-`