Inconsistent use of mean & sum when calculating KL divergence?

There is a mean taken inside [BaseVariationalLayer_.kl_div()](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/layers/base_variational_layer.py). But later a sum is used inside [get_kl_loss()](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/models/dnn_to_bnn.py) & when reducing the KL loss of a layer's bias & weights (e.g. inside [Conv2dReparameterization.kl_loss()](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/layers/variational_layers/conv_variational.py)).

I'm wondering if there is mathematical justification for this? Why take the mean of the individual weight KL divergences only to later sum across layers?