The only sentence about “absorbing” the BatchNorm layer I could find is this one

We  remove LRN layers  and  add BatchNorm layers after every convolution layer. After pre-training, we remove BatchNorm layers by absorbing the parameters into the preceding conv layers.

I don’t know, what “absorbing” means, but maybe they simply remove the BatchNorm layers and add its parameters to the Conv layers?
In my opinion, this makes only sense, if you set bias=False in your Conv layers, which can be done anyway, if the BatchNorm layer is used with affine=True.
In this approach, you could scale the conv weights with the batchnorm weights and just add the batchnorm bias. I’m not sure, how to deal with the running stats (mean and var).
However, I don’t see any reason to do this. Did you find any more information in the paper?
Also, is there a reference implementation?