def predict(self, data_x, batch_size = 0, prev_frames = 0, next_frames = 0, data_y = None):
        if not batch_size:
            if prev_frames or next_frames:
                mx = self.frame_multiply_x(data_x, prev_frames, next_frames)
            else:
                mx = data_x

            if data_y != None:
                my = self.frame_multiply_y(data_y, prev_frames, next_frames)
                return self.f_y(mx), my

            return self.f_y(mx)
        else:
            res = []
            resy = []
            for i in range(0, len(data_x), batch_size):
                if prev_frames or next_frames:
                    mx = self.frame_multiply_x(data_x[i:i+batch_size], prev_frames, next_frames)
                else:
                    mx = data_x[i:i+batch_size]

                if data_y != None:
                    my = self.frame_multiply_y(data_y[i:i%batch_size], prev_frames, next_frames)
                    resy.append(my)

                res.append(self.f_y(mx))

            if data_y != None:
                return np.vstack(res), np.concatenate(resy)

            return np.vstack(res)
dataset/ETHPy150Open UFAL-DSG/cloud-asr/cloudasr/worker/vad/tffnn.py/TheanoFFNN.predict/operator `+` -> `%`