Can a neural network be of variable depth? It is very common for neural networks to be asymmetric about the x axis, that is, to have many more nuerons in the first few layers than in the last few layers. Common example:

But can neural networks be structured in such a way that they're asymmetric about the y axis, that is, such that some parts of the network are 'deeper' than others?
An example of where this could be useful is where a problem involves some unsophisticated features (that require only a small number of layers), and some much more sophisticated features (which may benefit greatly from having more layers - i.e. greater 'depth').
&quot;Wide and Deep network&quot;  was used in a 2016 paper.

$\hspace{5cm}$Wide &amp; Deep Learning for Recommender Systems
You may create using keras Concatenate layer
concat = keras.layers.Concatenate()([input, hidden2])


References to read -
The paper
Google blog
Another paper