def prepare(self):
        self.output_dim = self.hidden_size

        self.W_h = self.create_weight(self.hidden_size, self.hidden_size, "h", initializer=self.inner_init)
        self.b_h = self.create_bias(self.hidden_size, "h")

        self.register_parameters(self.W_h, self.b_h)

        self.input_weights = []
        if self._input_type == "sequence":
            all_input_dims = [self.input_dim] * self.additional_input_dims
            for i, input_dim in enumerate(all_input_dims):
                wi = self.create_weight(input_dim, self.hidden_size, "wi_{}".format(i+1), initializer=self.outer_init)
                weights = [wi]
                self.input_weights.append(weights)
                self.register_parameters(*weights)
dataset/ETHPy150Open zomux/deepy/deepy/layers/recurrent.py/RNN.prepare/operator `+` -> `*`