About nn.KLDivLoss() input

Hi, all.
As far as I know, 1st parameter in nn.KLDivLoss() need to be F.log_softmax().
What about the 2nd parameter?
(In my case, there is no softmax in classifier.)

class Classifier(nn.Module):
    def __init__(self, channel, classes=10):
        super(Classifier, self).__init__()
        self.fc = nn.Linear(channel, classes)

    def forward(self, x):
        x = self.fc(x)
        return x
pred1 = classifier(input)
pred2 = classifier(input)
1) kld_loss = nn.KLDivLoss()(F.log_softmax(pred1, dim=1), pred2)
2) kld_loss = nn.KLDivLoss()(F.log_softmax(pred1, dim=1), F.softmax(pred2, dim=1))

Based on the docs, the second argument should be passed as probabilities:

As with NLLLoss , the input given is expected to contain log-probabilities and is not restricted to a 2D Tensor. The targets are given as probabilities (i.e. without taking the logarithm).