Understanding backprop for softmax I'm looking on a given solution of the first assignment of cs231n course.

Down below a snippet from the loss function.
I don't really understand lines 140-143. Can you explain why dscores (the derivative of scores) is calculated like that?
Be aware that posting code in images very annoying to copy/paste and it's bad for web reference ment.

This is due to the derivative of the softmax, but to me it's seems fishy.

If $S$ is the softmax vector, then the Jacobian $DS$ consists of $S_j(\delta_{ij}-S_i)$. This could explain the -=1 part, but not the /=N, and not the shape either.