Generates a CNN.
        Args:
            model_len: An integer. Number of convolutional layers.
            model_width: An integer. Number of filters for the convolutional layers.
        Returns:
            An instance of the class Graph. Represents the neural architecture graph of the generated model.

def generate(self, model_len=None, model_width=None):
        """Generates a CNN.
        Args:
            model_len: An integer. Number of convolutional layers.
            model_width: An integer. Number of filters for the convolutional layers.
        Returns:
            An instance of the class Graph. Represents the neural architecture graph of the generated model.
        """

        if model_len is None:
            model_len = Constant.MODEL_LEN
        if model_width is None:
            model_width = Constant.MODEL_WIDTH
        pooling_len = int(model_len / 4)
        graph = Graph(self.input_shape, False)
        temp_input_channel = self.input_shape[-1]
        output_node_id = 0
        stride = 1
        for i in range(model_len):
            output_node_id = graph.add_layer(StubReLU(), output_node_id)
            output_node_id = graph.add_layer(
                self.batch_norm(graph.node_list[output_node_id].shape[-1]), output_node_id
            )
            output_node_id = graph.add_layer(
                self.conv(temp_input_channel, model_width, kernel_size=3, stride=stride),
                output_node_id,
            )
            temp_input_channel = model_width
            if pooling_len == 0 or ((i + 1) % pooling_len == 0 and i != model_len - 1):
                output_node_id = graph.add_layer(self.pooling(), output_node_id)

        output_node_id = graph.add_layer(self.global_avg_pooling(), output_node_id)
        output_node_id = graph.add_layer(
            self.dropout(Constant.CONV_DROPOUT_RATE), output_node_id
        )
        output_node_id = graph.add_layer(
            StubDense(graph.node_list[output_node_id].shape[0], model_width),
            output_node_id,
        )
        output_node_id = graph.add_layer(StubReLU(), output_node_id)
        graph.add_layer(StubDense(model_width, self.n_output_node), output_node_id)
        return graph