Hi matilu, not sure if it’s useful, but I made a video on forwardprop/backwardprop, chain rule, and corresponding pytorch code, not sure if it could be useful?  There are a few parts, so you can just choose the part(s) that work for you.  I’m doing it in context of rnn, but it’s probably generally applicable.
part 1: rnn introduction, forward prop https://www.youtube.com/watch?v=ppJwiv1qtlE 3
part 2: backprop concepts https://www.youtube.com/watch?v=cxZFt6DJp90 3
part 3: why do we want to backprop? https://www.youtube.com/watch?v=pSXu7uXXB9I 1
part 4: backprop maths, chainrule (sort of like your diagram above, maybe? https://www.youtube.com/watch?v=0bjONLIU2No 1
part 5: just define the python vars gradOutput, gradWeight ,that I’m using https://www.youtube.com/watch?v=6GOA4LqHyP8 2
part 6: write out backprop by hand, in python, without using autograd https://www.youtube.com/watch?v=3tfRbELS8Zg
part 7: wirting backprop using autograd, one line  https://www.youtube.com/watch?v=g_sER3OJSjA 2