Continuing the discussion from Performance and allocation issue with arrays of functions (v1.5):

I want to modify the question I originally posed above, to the case that the collection of functions on which I wish to evaluate may, themselves, be generated. I am thinking of the following example:

```
@generated function compute_values(x₀, Δt, nΔt, f::Tuple{Vararg{<:Any,K}}) where {K}
quote
x = x₀;
f_vals = zeros($K, nΔt)
for n in 1:nΔt
x += 0.5 * Δt * x
Base.Cartesian.@nexprs $K k -> f_vals[k,n] = (f[k])(x);
end
return f_vals
end
end
x₀ = 1.0;
Δt = 0.5;
nΔt = 10^2;
f_tuple = (x->x.^p for p in 1:5)
compute_values(x₀, Δt, nΔt, f_tuple)
```

but this gives the error:

```
MethodError: no method matching compute_values(::Float64, ::Float64, ::Int64, ::Base.Generator{UnitRange{Int64}, var"#3#5"})
Closest candidates are:
compute_values(::Any, ::Any, ::Any, ::Tuple{Vararg{Any, K}}) where K at In[3]:1
Stacktrace:
[1] top-level scope
@ In[6]:1
[2] eval
@ ./boot.jl:360 [inlined]
[3] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
@ Base ./loading.jl:1094
```

Any recommendations on how to handle this?