I’ve probably phrased it wrong.
The output of a linear layer, which can be used for a multi-class classification use case, will return the logits, which are raw prediction values in the range [-Inf, +Inf].
Internally nn.CrossEntropyLoss, which is usually used for this classification use case, will apply F.log_softmax and nn.NLLLoss, so the raw prediction values will be transformed to log probabilities.