I need a generalizable solution to the following problem:
A neural network has multiple inputs, for example some sort of image (A) which I want to use some convolution layers on etc, and some numerical values (B).
(A) and (B) should at some point feed into the same layer(s) © and eventually produce a result. Graphically it might look like this
A1 -> A2 -> A3 \
                           C1 -> C2 -> C3
B1 -> B2 -> B3 /

In keras this would be solved by creating a model out of the (A) layers, creating a second model out of the (B) layers and calling keras.layers.concatenate on them.
Is something similar possible by stacking torch’s Sequential models and if so, how? The examples I’ve seen use classes derived from nn.Module which doesn’t fit my needs unfortunately.