def METHOD_NAME():
    master_seed(1234)
    model = Sequential()
    model.add(Dense(100, activation="relu", input_dim=10))
    model.add(Dense(10, activation="relu"))
    model.add(Dense(1))
    model.compile(loss="mean_squared_error", optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=["accuracy"])
    (x_train, y_train), (_, _), _, _ = load_dataset("diabetes")
    model.fit(x_train, y_train, batch_size=128, epochs=10)
    w_0, b_0 = model.layers[0].get_weights()
    w_1, b_1 = model.layers[1].get_weights()
    w_2, b_2 = model.layers[2].get_weights()
    np.save(
        os.path.join(os.path.dirname(os.path.dirname(__file__)), "resources/models/", "W_DENSE1_DIABETES"),
        w_0,
    )
    np.save(
        os.path.join(os.path.dirname(os.path.dirname(__file__)), "resources/models/" "B_DENSE1_DIABETES"),
        b_0,
    )
    np.save(
        os.path.join(os.path.dirname(os.path.dirname(__file__)), "resources/models/" "W_DENSE2_DIABETES"),
        w_1,
    )
    np.save(
        os.path.join(os.path.dirname(os.path.dirname(__file__)), "resources/models/" "B_DENSE2_DIABETES"),
        b_1,
    )
    np.save(
        os.path.join(os.path.dirname(os.path.dirname(__file__)), "resources/models/" "W_DENSE3_DIABETES"),
        w_2,
    )
    np.save(
        os.path.join(os.path.dirname(os.path.dirname(__file__)), "resources/models/" "B_DENSE3_DIABETES"),
        b_2,
    )