I have the following Tensorflow model that I want to quantize:

model = Sequential([
Input(shape=input_shape),
LSTM(lstm_units_1, return_sequences=True),
Dropout(dropout_rate),
LSTM(lstm_units_2, return_sequences=False),
Dropout(dropout_rate),
Dense(4, activation=’softmax’)
])

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
model_checkpoint = ModelCheckpoint(model_path, monitor='val_loss', save_best_only=True, save_weights_only=False, mode='min')

history = model.fit(X_train, y_train, 
                    epochs=epochs, 
                    batch_size=batch_size, 
                    validation_split=0.2, 
                    callbacks=[early_stopping],
                    verbose=1)

model.save(model_path)

I am trying to perform the quantization like this:

annotated_model = tfmot.quantization.keras.quantize_annotate_model(model)

with tfmot.quantization.keras.quantize_scope():
    quant_aware_model = tfmot.quantization.keras.quantize_apply(annotated_model)

quant_aware_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

But I am receiving this error: ValueError: `to_annotate` can only be a `keras.Model` instance. Use the `quantize_annotate_layer` API to handle individual layers. You passed an instance of type: Sequential.

Trying to quantize each layer as the error suggest didn’t work for me as well with another value error about LSTM layers not being accepted as inputs.

annotated_model = tf.keras.Sequential([
    tfmot.quantization.keras.quantize_annotate_layer(layer)
    for layer in model.layers
])

What is the correct way to quantize the particular model I am using here?