def METHOD_NAME(data):
    inputs = tf.keras.Input(shape=(np.shape(data)[1],))  # create input layer
    layers = mea_column_model_customnormform_savedmodel(  # define the rest of network using our custom class
        input_labels=xlabels,
        output_labels=zlabels,
        input_bounds=xdata_bounds,
        output_bounds=zdata_bounds,
        normalized=True,
        normalization_form="Custom",
        normalization_function="(datavalue - dataminimum)/(datamaximum - dataminimum)",
    )
    outputs = layers(inputs)  # use network as function outputs = f(inputs)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)  # create model
    model.compile(loss="mse", optimizer="RMSprop", metrics=["mae", "mse"])
    model.fit(xdata, zdata, epochs=500, verbose=0)  # train model
    return model