Model with SeparableConvs not converting to QAT

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
The model with SeparableConv2D is not getting converted to QAT model with quantize_apply function

System information
Linux Ubuntu 20.04 LTS, Python version: 3.8, CUDA/cuDNN version: 11.4, GPU model and memory: NVIDIA GeForce RTX 2060, 6144 MB

TensorFlow version (installed from source or binary): tf-nightly

TensorFlow Model Optimization version (installed from source or binary): 0.6.0

Python version: 3.8

Describe the expected behavior
The model should get converted to a QAT model

Describe the current behavior
The model doesn't get converted to a QAT model

Code to reproduce the issue
It can be found in this colab

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

Additional context
I need my model to work. It's a critical issue.