What is the right way to construct and then run a Julia function programmatically?

General Usage
13

Anonymous functions optimize just fine

julia> function makefun()
    f1 = x->x+2
    f2 = x->(f1(f1(x)))
    return f2
end

makefun (generic function with 1 methods)

julia> aa = makefun();
julia> @code_typed aa(3)

CodeInfo(
│╻╷ #74 1 ─ %1 = (Base.add_int)(x, 2)::Int64
││╻  +  │   %2 = (Base.add_int)(%1, 2)::Int64
│    └──      return %2
) => Int64

julia>  bb(x) = x+2+2;
julia> @code_typed bb(3)

CodeInfo(
│╻╷ +1 1 ─ %1 = (Base.add_int)(x, 2)::Int64
││┃  +  │   %2 = (Base.add_int)(%1, 2)::Int64
│    └──      return %2
) => Int64

Noted that aa and bb get lowered to exactly the same thing.

Is it feasible to use Flux.jl (or some other existing automatic differentiation package) on huge chains of anonymous functions like this?

Yes.
(well flux isn’t AD, but Flux will invoke Tracker.jl (or oneday Zygote.jl) and both have no issue with this.)

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