Thank you for the response, Jerry.
So, what should I do with the bias parameter of the batch-norm module when I want to implement my quantized model on hardware? the final converted model (quantized) still has this parameter (in FP) in the quantized version of ConvBnReLU2d.

Would bias be totally ignored when we recall the quantized model for some input X  (model.eval() )?
or the intermediate feature values are temporarily converted to FP to apply bias to them and then are converted back to INT8/INT32?
or bias is also converted to INT8 with a simple choice of sale or zero-point without the influence of the qat part?