I was confused by the pattern as well, but it makes sense if you understand the parent class. I figured it works as such:
    super(_DenseLayer, self).forward(x) 

calls on the forward() method of the parent class of _DenseLayer (nn.Sequential) through the super keyword, which is a more elegant way to reference the parent class from which a class inherits. The source code of torch.nn.Sequential.forward() has following code in place for the method:
    def forward(self, input):
       for module in self._modules.values():
           input = module(input)
       return input

So it takes all the modules which were added to the class using self.add_module and passes the values through each model step, which it then uses for the inputs again. Essentially, this lets it execute the entire block without using a bunch of function calls to clutter the code.
Hopefully this puts the question to rest!