def METHOD_NAME():
    actuals = tf.constant(
        [
            [1, 0, 0, 0, 0],
            [0, 0, 0, 1, 0],
            [0, 0, 0, 0, 1],
            [0, 1, 0, 0, 0],
            [0, 0, 1, 0, 0],
            [0, 0, 1, 0, 0],
            [1, 0, 0, 0, 0],
            [0, 1, 0, 0, 0],
        ],
        dtype=tf.float32,
    )
    predictions = tf.constant(
        [
            [0.85, 0, 0.15, 0, 0],
            [0, 0, 0, 1, 0],
            [0, 1, 0, 0, 0],
            [0.05, 0.90, 0.04, 0, 0.01],
            [0.10, 0, 0.81, 0.09, 0],
            [0.10, 0.045, 0, 0.81, 0.045],
            [1, 0, 0, 0, 0],
            [0, 0.85, 0, 0, 0.15],
        ],
        dtype=tf.float32,
    )
    # Initialize
    hl_obj = HammingLoss("multiclass", threshold=0.8)
    hl_obj.update_state(actuals, predictions)
    # Check results
    check_results(hl_obj, 0.25)