Here, is 10 the number of bi-classifications ?
If it is :
Since it is bi-classification, you could instead get an output shape of [10, 1] and use nn.BCEWithLogitsLoss. It combines Sigmoid + BinaryCrossEntropy (so do not use a sigmoid activation just before that).
For the multiple classification aspect I suppose your output shape is [batch_size, 10, 1], and your label shape is also [batch_size, 10, 1] filled with ones and zeros. The loss function by default will average over the 10 different tasks, which is probably what you want.