Can Pytorch handle backprop to separate branches if you concatenate the output of two branches into a single linear layer and then proceed to go deeper in the network until you calculate a final output?
For example:

Branch_1 takes channel 1 of the input image and performs convolutions.
Branch_2 takes channel 2 of the input image and performs convolutions.
Then the outputs of each branch are converted into 1D arrays and concatenated.
More linearly connected layers proceed the concatenation.
Finally some output value or class values are output by the network.

(Note: This is just an example to illustrate the use of branched networks for two input images)
Many thanks in advance!