No, @ptrblck I am talking about absolutely new algorithm ANTI-GRAD. Sorry, when I am in euphoria I can’t write properly. If you or anybody else doubt it somehow, please, state here.
What is ANTI-GRAD - it’s an absolutely new algorithm that trains labels looking for an integral to unfold mathematical formula behind the neural network. It’s opposite to auto-grad that calculate s derivatives. My algorithm calculates integral.
Why it’s doing that?
To find a formula that describes a neural network.
Why do we need that?

We can create new layers.
We can enhance neuroscience by using classical mathematics to describe the neural network.

So it’s absolutely new approach. We have to do the following to make it work:

We have to create an ANTI-GRAD algorithm that finds an integral of a given formula.
We have to create a mechanism that makes it possible to train labels in search for integral.
We have to control loss between out neural network and labels that we are training. So, when loss is close to 0 we have a classical mathematical formula that describes a neural network.
We have to make a mechanism to turn the complex mathematical formula into simple one for easy conversion to a new type of layer. “Simple is better than complex” - Zen of Python.