The basic building blocks of deep networks are of the form: Linear layer + Point-wise non-linearity / activation.
Keras rolls these two into one, called “Dense.”
(I’m not sure why the Keras example you have follows Dense with another activation, that doesn’t make sense to me.)
To make a simple multi-layer perception in PyTorch you should stack nn.Linear (a simple linear layer that computes w^Tx + b) and nn.ReLU.
If you’d like a softmax followed by cross entropy loss at the end, you can use CrossEntropyLoss (which performs the softmax and the loss in one function for numerical reasons).