So rather than using the ResNet architecture directly you can still take inspiration from the architecture by adding things like residual connections. If you take a look at the BasicBlock in ResNet you can try a variation of the architecture but with fully connecter layers rather than conv layers (the important part is the use of identity to add the original input back to the output which is the residual connection).
Transformers are not ResNets but they are just another example of an architecture that uses a residual connection.
Typically the size of the model embedding is grown from the input layers but in this case all of your layer sizes have dimension smaller than the input. So I just mean that usually his dimension is much larger in the MLP.