for weights, it’s quantized when we swap the floating point conv to quantized conv, it can be done through attaching a qconfig to the conv module instance
there might still be some misunderstanding, a quantized module takes int8 Tensor as input and outputs a int8 Tensor as well, so if previous linear/activation layer is quantized (meaning we attach a int8 qconfig to that layer), we do not need to put a QuantStub in between the two layers.