You have different options for a binary classification use case:

you could use 1 output neuron and [nn.Sigmoid + nn.BCELoss]
1 output neuron and [raw logits (no non-linearity for the last layer) + nn.BCEWithLogitsLoss]
2 output neurons and [nn.LogSoftmax + nn.NLLLoss]
2 output neurons and [raw logits + nn.CrossEntropyLoss]

So basically you can decide, if you want to use one neuron or two for a binary classification task.