Trying to quantise MobileNetv3 small Exception encountered when calling layer "tf.__operators__.add_137"

There has recently been an article saying mobilenetv3 can be used for QAT
https://blog.tensorflow.org/2022/06/Adding-Quantization-aware-Training-and-Pruning-to-the-TensorFlow-Model-Garden.html

However when I run this code:

from tensorflow.keras.applications import MobileNetV3Small
import tensorflow_model_optimization as tfmot

model = MobileNetV3Small(
    input_shape=(224, 224, 3),
    include_top=True,
    weights=None,
    classes=200,
)

qat_model = tfmot.quantization.keras.quantize_model(model)

I get this error:

AttributeError: Exception encountered when calling layer "tf.__operators__.add_56" (type TFOpLambda).

'list' object has no attribute 'dtype'

Call arguments received by layer "tf.__operators__.add_56" (type TFOpLambda):
  • x=['tf.Tensor(shape=(None, 112, 112, 16), dtype=float32)']
  • y=3.0
  • name=None

I am using an m1 Mac:
conda
Python 3.10.4
tensorflow-macos 2.9.2
tensorflow-metal 0.5.0
tensorflow-model-optimization 0.7.2

Reproduce the exception with MobileNetV3Small and tfmot.quantization.keras.quantize_model using the listed TensorFlow and optimization-tool versions. Trace how the quantization path handles the TFOpLambda addition, then verify that the model quantizes successfully and that a regression test covers this case.