def METHOD_NAME(input_shapes):
    inputs = []
    for i, input_shape in enumerate(input_shapes):
        inputs.append(tf.keras.Input(shape=input_shape[1:], name="input_{}".format(i + 1)))
    # pylint: disable=unbalanced-tuple-unpacking
    input_1, input_2 = inputs
    x_1 = input_1 * input_1
    x_2 = input_2 * input_2
    cat_1 = layers.Concatenate(1)([x_1, x_2])
    cat_2 = layers.Concatenate(1)([x_1, cat_1])
    outputs = layers.Conv2D(filters=3, kernel_size=3, strides=2, padding="same")(cat_2)
    return tf.keras.Model(inputs=inputs, outputs=outputs)