Re: Normalized AST presentation of the proc body

[mratsim]

You might be interested in my autograd library: 
[nim-rmad](https://github.com/mratsim/nim-rmad).

It automatically compute the gradient of any function with regards to any of 
its input. It uses reverse-mode auto-differentiation behind the scene.
    
    
    let ctx = newContext[float32]()
    
    let
        a = ctx.variable(1)
        b = ctx.variable(2)
        c = ctx.variable(-3)
        x = ctx.variable(-1)
        y = ctx.variable(3)
    
    proc forwardNeuron[T](a,b,c,x,y: T): T =
        let
            ax = a * x
            by = b * y
            axpby = ax + by
            axpbypc = axpby + c
            s = axpbypc.sigmoid()
        return s
    
    var s = forwardNeuron(a,b,c,x,y)
    
    echo s.value() # 0.8807970285415649
    
    let gradient = s.grad()
    echo gradient.wrt(a) # -0.1049936264753342
    echo gradient.wrt(b) # 0.3149808645248413
    echo gradient.wrt(c) # 0.1049936264753342
    echo gradient.wrt(x) # 0.1049936264753342
    echo gradient.wrt(y) # 0.2099872529506683