def METHOD_NAME():
    # Load MNIST dataset
    mnist = tf.keras.datasets.mnist
    (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
    # Normalize the input image so that each pixel value is between 0 to 1.
    train_images = train_images / 255.0
    test_images = test_images / 255.0
    # Define the model architecture.
    model = tf.keras.Sequential(
        [
            tf.keras.layers.InputLayer(input_shape=(28, 28)),
            tf.keras.layers.Reshape(target_shape=(28, 28, 1)),
            tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation="relu"),
            tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(10),
        ]
    )
    # Train the digit classification model
    model.compile(
        optimizer="adam", loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=["accuracy"]
    )
    model.fit(
        train_images,
        train_labels,
        epochs=1,
        validation_split=0.1,
    )
    _, baseline_model_accuracy = model.evaluate(test_images, test_labels, verbose=0)
    print("Baseline test accuracy:", baseline_model_accuracy)
    model.save("baseline_model")