Failed to apply the QAT function 'quantize_model' to the sequential model that is defined using tensorflow.keras

Start by reproducing the failure with the TensorFlow 2.17.0, TensorFlow Model Optimization 0.8.0, and Python 3.10 setup shown in the issue. Inspect tensorflow_model_optimization/python/core/quantization/keras/quantize.py around quantize_model and its Sequential check, then verify the supported tensorflow.keras model path with a focused test. Done means the supplied Sequential model is accepted by quantize_model without the reported ValueError.

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
A clear and concise description of what the bug is.

System information

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

Describe the expected behavior

Describe the current behavior
Failed with the error

  File "/home/jamesbond/work/venv/lib/python3.10/site-packages/tensorflow_model_optimization/python/core/quantization/keras/quantize.py", line 135, in quantize_model
    raise ValueError(
ValueError: `to_quantize` can only either be a keras Sequential or Functional model.

Code to reproduce the issue

import tensorflow as tf
print(tf.__version__)

import tensorflow_model_optimization as tfmot
print(tfmot.__version__)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

def create_model():

# Define a simple Sequential model
    model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
    ])

    return model

to_quantize_model = create_model()
print(f'Type of the model is {type(to_quantize_model)}')

# Check that the model is sequential
if not isinstance(to_quantize_model, Sequential):
    raise ValueError('not sequantial')

# Check the whole condition from https://github.com/tensorflow/model-optimization/blob/ed3f0176b561fe693a3cc55b53a3605b943b6bbf/tensorflow_model_optimization/python/core/quantization/keras/quantize.py#L135
if not isinstance(to_quantize_model, Sequential) and not (
      hasattr(to_quantize_model, '_is_graph_network')
      and to_quantize_model._is_graph_network
  ):  # pylint: disable=protected-access
    raise ValueError(
        'Condition FAILED: `to_quantize` can only either be a keras Sequential or '
        'Functional model.'
    )

# But now this API fails with the condition above
qat_model = tfmot.quantization.keras.quantize_model(to_quantize_model)

Screenshots
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Additional context
My model is defined using tensorflow.keras instead of tensorflow_model_optimization.python.core.keras.compat like in tutorials and this leads to this error as model is not recognized as Sequential, although it is Sequential.