def METHOD_NAME():
    model = tf.keras.models.Sequential([
        tf.keras.layers.Input(shape=(IMAGE_SIZE, IMAGE_SIZE), name='images'),
        tf.keras.layers.Flatten(input_shape=(IMAGE_SIZE, IMAGE_SIZE)),
        tf.keras.layers.Dense(HIDDEN_SIZE, activation='relu'),
        tf.keras.layers.Dropout(DROPOUT),
        tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')
    ])
    model.compile(
        optimizer='adam',
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy'])
    return model