How to understand the gradient backward propagation of perceptual loss? about fast-neural-style HOT 1 OPEN

I guess this is explained by the perceptual losses being evaluated, not at the network output, but in content and style loss layers which have been inserted inside the network at selected layers. So we pass a zero gradient from the output backwards, and the real gradients will be added by the content and style loss layers.

At least this is what happens in the original, iterative neural-style, and I have the impression that fast-neural-style uses the same approach for perceptual loss evaluation. In other words, you should look at the content and style loss layers to see what they do to the gradient.

from fast-neural-style.