Does ProxylessNas implementation really support optimizing inference latency?

Environment:

• NNI version: v1.9 (including current master)
• NNI mode (local|remote|pai):
• Client OS:
• Server OS (for remote mode only):
• Python version:
• PyTorch/TensorFlow version:
• Is conda/virtualenv/venv used?:
• Is running in Docker?:

Log message:

• nnimanager.log:
• dispatcher.log:
• nnictl stdout and stderr:

What issue meet, what's expected?:

The most important feature of ProxylessNas is that it can balance deployment latency and accuracy with simple regularization parameters. But this feature is clearly missing in nni. I only found loss = criterion(outputs, labels) and loss.backward() where the criterion is just a cross-entropy loss and only applicable for image classification.

Would nni team consider adding this feature? If not, would you mind I writing this feature and pulling a request?

How to reproduce it?:

Additional information: