That would be strange, as the linear layers are currently just a single one.
Sorry for not being clear enough.
For a basic multi-class classification task you would have two options:

raw logits (i.e. no non-linearity at the end of your model, just the linear layer outputs) + nn.CrossEntropyLoss


nn.LogSoftmax as the last layer + nn.NLLLoss.

Both options are equivalent, as nn.CrossEntropyLoss calls nn.LogSoftmax + nn.NLLLoss internally.