Thanks so much for this very thorough explanation. So, if I understand correctly, this achieves what is described in the documentation (parameters drawn from uniform distribution bounded by 1/sqrt(in_features)), but in a kind of circuitous way. Although this approach uses the init.kaiming_uniform_ function, it is not actually Kaiming initialization (in the scenario where the subsequent nonlinearity is a ReLU). To get Kaiming initialization for a ReLU layer, one would need to re-initialize the weights using init.kaiming_uniform_ with nonlinearity set to ‘relu’. init Is that correct?