where is the loss gradient calculated in the proponent/opponent example

[Helaly96]

unlike the colab example of self influence where the gradient of the loss is clearly calculated using tape, i don't see where the loss_grad is being calculated in the proponent/opponent example loss_grad = tf.one_hot(labels, 1000) - probs this is just the loss with no gradients, and it's not calculated anywhere else can someone clarify?

[gumityolcu]

I also need this, especially can someone clarify where the random projection trick is being done ?

Thanks

[SeanZh30]

I am also a little confuesed about this I check the other issues discussed by author. He mentioned that in #6 we can get the influence(tracin score) = lg_sim (error_similarity) * a_sim (encoding similarity) according to the appendix F. This seems a bit confusing to me. If anyone could show me the derivation process, I would be very grateful.