def METHOD_NAME():
    model = tf.keras.Sequential(
        [
            tf.keras.Input(shape=(28, 28)),
            tf.keras.layers.Reshape(target_shape=(28, 28, 1)),
            tf.keras.layers.Conv2D(32, 3, activation="relu"),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(128, activation="relu"),
            tf.keras.layers.Dense(10),
        ]
    )
    model.compile(
        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
        optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),
        metrics=["accuracy"],
    )
    return model