Building a simple CNN using tf.keras functional API

simple_cnn.py

from tensorflow import keras

# Creating a simple CNN model in keras using functional API
def create_model():
    img_inputs = keras.Input(shape=IMG_SHAPE)
    conv_1 = keras.layers.Conv2D(32, (3, 3), activation='relu')(img_inputs)
    maxpool_1 = keras.layers.MaxPooling2D((2, 2))(conv_1)
    conv_2 = keras.layers.Conv2D(64, (3, 3), activation='relu')(maxpool_1)
    maxpool_2 = keras.layers.MaxPooling2D((2, 2))(conv_2)
    conv_3 = keras.layers.Conv2D(64, (3, 3), activation='relu')(maxpool_2)
    flatten = keras.layers.Flatten()(conv_3)
    dense_1 = keras.layers.Dense(64, activation='relu')(flatten)
    output = keras.layers.Dense(metadata.features['label'].num_classes, activation='softmax')(dense_1)

    model = keras.Model(inputs=img_inputs, outputs=output)
    
    return model

nice

How to call create_model
Is it
x = create_model()
y = x(image)