Hello

I am getting the following error when trying to do quantization aware training with tensorflow 2.7:

```
ValueError: `to_quantize` can only either be a tf.keras Sequential or Functional model.
```

The error occurs when calling this method:

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

The model is defined below. I suppose the reason is that subclassed models are not supported? I have already trained(normal training, not QAT) multiple models with the definition below. Post-training quantization works, but i would like to try quantization aware training to see if it improves performance. Is there a way to be able to do quantization aware training with the model below, or alternatively define it in another way and redo normal training.

```
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Dense, Flatten, Activation, Dropout
from tensorflow.keras.models import Model
class Mobilenet_v2_transfer(Model):
def __init__(self):
super(Mobilenet_v2_transfer, self).__init__()
self.base = tf.keras.applications.mobilenet_v2.MobileNetV2(
input_shape=(224, 224, 3), alpha=1.0, include_top=False, weights='imagenet',
pooling='avg')
self.base.trainable = True
for layer in self.base.layers[:130]:
layer.trainable = False
self.flatten = Flatten()
self.dense = Dense(1, kernel_regularizer=tf.keras.regularizers.L2(0.01)
self.sigmoid = Activation('sigmoid')
def call(self, x):
x = self.base(x)
x = self.flatten(x)
x = self.dense(x)
x = self.sigmoid(x)
return x
```