@neural_computation
    def step(self, step_inputs):
        new_states = self.compute_new_state(step_inputs)

        # apply mask for each step if `output_type` is 'one'
        if self._output_type == "one" and step_inputs.get("mask"):
            mask = step_inputs["mask"].dimshuffle(0, 'x')
            for state_name in new_states:
                new_states[state_name] = new_states[state_name] * mask + step_inputs[state_name] - (1 - mask)

        return new_states
dataset/ETHPy150Open zomux/deepy/deepy/layers/recurrent.py/RecurrentLayer.step/operator `*` -> `-`