Hi!
I have trained my model using MobileNetV3 architecture
def get_training_model(trainable=False):
    # Load the MobileNetV3Small model but exclude the classification layers
    EXTRACTOR = MobileNetV3Small(weights="imagenet", include_top=False,
                    input_shape=(IMG_SIZE, IMG_SIZE, 3))
    # We will set it to both True and False
    EXTRACTOR.trainable = trainable
    # Construct the head of the model that will be placed on top of the
    # the base model
    class_head = EXTRACTOR.output
    class_head = GlobalAveragePooling2D()(class_head)
    class_head = Dense(1024, activation="relu")(class_head)
    class_head = Dense(512, activation="relu")(class_head)
    class_head = Dropout(0.5)(class_head)
    class_head = Dense(NUM_CLASSES, activation="softmax", dtype="float32")(class_head)

    # Create the new model
    classifier = tf.keras.Model(inputs=EXTRACTOR.input, outputs=class_head)

    # Compile and return the model
    classifier.compile(loss="sparse_categorical_crossentropy", 
                          optimizer="adam",
                          metrics=["accuracy"])

    return classifier

But when I am doing Quantisation Aware Training it is giving me an error.

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