Add a weighted add skip-connection from after start node to end node.
        Args:
            start_id: The convolutional layer ID, after which to start the skip-connection.
            end_id: The convolutional layer ID, after which to end the skip-connection.

def to_add_skip_model(self, start_id, end_id):
        """Add a weighted add skip-connection from after start node to end node.
        Args:
            start_id: The convolutional layer ID, after which to start the skip-connection.
            end_id: The convolutional layer ID, after which to end the skip-connection.
        """
        self.operation_history.append(("to_add_skip_model", start_id, end_id))
        filters_end = self.layer_list[end_id].output.shape[-1]
        filters_start = self.layer_list[start_id].output.shape[-1]
        start_node_id = self.layer_id_to_output_node_ids[start_id][0]

        pre_end_node_id = self.layer_id_to_input_node_ids[end_id][0]
        end_node_id = self.layer_id_to_output_node_ids[end_id][0]

        skip_output_id = self._insert_pooling_layer_chain(start_node_id, end_node_id)

        # Add the conv layer
        new_conv_layer = get_conv_class(self.n_dim)(filters_start, filters_end, 1)
        skip_output_id = self.add_layer(new_conv_layer, skip_output_id)

        # Add the add layer.
        add_input_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
        add_layer = StubAdd()

        self._redirect_edge(pre_end_node_id, end_node_id, add_input_node_id)
        self._add_edge(add_layer, add_input_node_id, end_node_id)
        self._add_edge(add_layer, skip_output_id, end_node_id)
        add_layer.input = [
            self.node_list[add_input_node_id],
            self.node_list[skip_output_id],
        ]
        add_layer.output = self.node_list[end_node_id]
        self.node_list[end_node_id].shape = add_layer.output_shape

        # Set weights to the additional conv layer.
        if self.weighted:
            filter_shape = (1,) * self.n_dim
            weights = np.zeros((filters_end, filters_start) + filter_shape)
            bias = np.zeros(filters_end)
            new_conv_layer.set_weights(
                (add_noise(weights, np.array([0, 1])), add_noise(bias, np.array([0, 1])))
            )