No, as given in the docs 8, the loss expects class indices instead:

The target that this loss expects should be a class index in the range [0,C−1] where C = number of classes; if ignore_index is specified, this loss also accepts this class index (this index may not necessarily be in the class range).

You can see the applied formula in the CrossEntropyLoss docs 25. Note that nn.CrossEntropyLoss will internally use log_softmax + NLLLoss.