I have used multiple losses that way (using a sum of the losses) with success. The loss.backward() call computes the gradients for all weights according to the result of the sum, and the gradients get back propagated with the optimizer later. So your loss is used in the backward pass.
One way to check if it “works” (the loss value decreases if it should decrease) is to plot it separately, as @MariosOreo said.
One other way to check if it “works” (helps in your current objective) is to try without your custom loss (remove it from the sum) and then compare the results (overall loss and accuracy or results if applicable).