def METHOD_NAME(self):
    """Simple Convnet model."""
    model = tf.keras.Sequential()
    model.add(
        tf.keras.layers.Conv2D(
            32,
            kernel_size=(3, 3),
            activation="relu",
            input_shape=(28, 28, 1),
        )
    )
    model.add(tf.keras.layers.Conv2D(64, (3, 3), activation="relu"))
    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
    model.add(tf.keras.layers.Dropout(0.25))
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(128, activation="relu"))
    model.add(tf.keras.layers.Dropout(0.5))
    model.add(tf.keras.layers.Dense(10, activation="softmax"))
    return model