Sequential provides a means to plug layers together in a feed-forward fully connected manner.

E.g. creating a one hidden-layer multi-layer perceptron is thus just as easy as: 

mlp = nn.Sequential()
mlp:add( nn.Linear(10, 25) ) -- 10 input, 25 hidden units
mlp:add( nn.Tanh() ) -- some hyperbolic tangent transfer function
mlp:add( nn.Linear(25, 1) ) -- 1 output

require "lab"
print(mlp:forward(lab.randn(10)))
which gives the output: 
-0.1815
[torch.Tensor of dimension 1]