def METHOD_NAME():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(64, activation="relu"))
    model.add(tf.keras.layers.Dense(64, activation="relu"))
    model.add(tf.keras.layers.Dense(1, activation="softmax"))
    mcc = MatthewsCorrelationCoefficient(num_classes=1)
    model.compile(
        optimizer="Adam", loss="binary_crossentropy", metrics=["accuracy", mcc]
    )
    # data preparation
    data = np.random.random((10, 1))
    labels = np.random.random((10, 1))
    labels = np.where(labels > 0.5, 1.0, 0.0)
    model.fit(data, labels, epochs=1, batch_size=32, verbose=0)