docs: clarification in README.md

Author: raphbrodeur

README.md

@@ -27,7 +27,7 @@ The resulting model behaves exactly like any standard `nn.Module`, but instead o
 ## Key Features
 
 - **One line to "BNN-ize" any model** — Turn any already existing PyTorch model into a BNN with a single line of code. No need to rewrite your model, redefine layers, or modify your existing architecture.
-- **Truly compatible with all layers** — Unlike other "BNN-izers" that swap specific supported layers for variational versions, torchbayesian converts every trainable parameter in your model into a variational posterior module, actually making the entire model Bayesian, not just parts of it.
+- **Truly compatible with all layers** — Unlike other "BNN-izers" that swap specific supported layers for variational versions (most often, only `nn.Linear` and `nn.ConvNd` layers are made variational), torchbayesian actually converts every trainable parameter in your model into a variational posterior `nn.Module`, actually making the entire model Bayesian, not just parts of it.
 - **PyTorch-native design** — Works entirely within PyTorch's framework; training, inference, evaluation remain unchanged. Fully compatible with other PyTorch-based tools such as [Lightning](https://lightning.ai/docs/pytorch/stable/), [TorchMetrics](https://lightning.ai/docs/torchmetrics/stable/), and [MONAI](https://monai.io/).
 - **Custom priors and variational posteriors** — Specify priors and variational posteriors directly as arguments. You can also define your own custom priors and variational posteriors and register them with the API using a simple decorator logic. This allows both plug-and-play use and deep customization without having to touch the core library.
 - **KL divergence easily accessible** — Retrieve the model's KL divergence at any point using the `.kl_divergence()` method of `bnn.BayesianModule`.