quantize_model() cannot detect a keras.Sequential model

[DKMaCS]

Prior to filing: check that this should be a bug instead of a feature request. Everything supported, including the compatible versions of TensorFlow, is listed in the overview page of each technique. For example, the overview page of quantization-aware training is here. An issue for anything not supported should be a feature request.

Describe the bug
I'm passing a keras sequential model into quantize_model(), and I'm getting the error that the model isn't a sequential model.

System information

Carried out in:
Google Colab

TensorFlow version (installed from source or binary):
2.17.0

TensorFlow Model Optimization version (installed from source or binary):
0.8.0

Python version:
3.10.12

Describe the expected behavior
prepare a keras sequential model built from scratch that was imported via load_model()

Describe the current behavior
ValueError: to_quantize can only either be a keras Sequential or Functional model.

Code to reproduce the issue
!pip install tensorflow_model_optimization

import pandas as pd
import numpy as np
import time
import tensorflow as tf
import os
import tempfile
import keras
import tensorflow_model_optimization as tfmot
from google.colab import drive
from tensorflow.keras.models import load_model

drive.mount('/content/drive')
%cd /content/drive/My Drive/CS528/HW3

model = load_model('/content/drive/My Drive/CS528/HW3/q1_model.keras')

quant_aware_model_tflite = '/content/drive/My Drive/CS528/HW3/s_mnist_quant_aware_training.tflite'
quantize_model = tfmot.quantization.keras.quantize_model
q_aware_model = quantize_model(model)

Screenshots
If applicable, add screenshots to help explain your problem.

Additional context
I've already checked the same compound conditional using the imported model just before calling quantize_model(), and it behaves as it should. Only when quantize_model() is actually handling the model does it seem to think the model isn't a tf.keras.Sequential object.

[DKMaCS]

q1_model.keras was made beforehand using a sequential model with common layers using the save_model() function from tensorflow.keras.models

[pedrofrodenas]

The problem is that you are using Keras3. To avoid this problem, download tf_keras library that match your tensorflow version.
import tf_keras as keras and set up os.environ["TF_USE_LEGACY_KERAS"] = "1"

[MEHDI342]

you could instead of importing the layers and sequential package this way`:
from keras.layers import LSTM, Dense
from keras.models import Sequential

do it this way instead:

from tensorflow.keras.layers import LSTM, Dense,
but for the sequential package you could just do a :
import keras
from keras import layers
from keras import ops
i think the import sequential is just a bug from gpt and claude , the layers and ops package are sufficient