I have a problem with an expensive intermediate computation that I have to use both within the normal model definition, and in a conditional Gibbs update - is there some way that I can reuse the computation directly in the conditional update?

Relevant subsections of code are (`M`

is a potentially expensive operation)

```
#this is inside the model definition
μ = μ₀ .* exp.(M(l, σ, w))
μi = S*μ
Turing.@addlogprob! sum(log.(μi[x.==1])) - hatsum(μ)*params.M.h
```

and

```
function cond_x_b_c(c, b::BranchingProcess{T}, catalog::Catalog{T}, M::MaternSPDE) where T
μ = c.μ₀ .* exp.(M(c.l, c.σ, c.w))
μi = S*μ
update_weights!(b, catalog, μi, c.K, c.α, c.c, c.p̃+1)
return Product([Categorical(b.bnodes[i].bweights) for i = 1:length(catalog.t)])
end
cond_x(c) = cond_x_b_c(c, b, catalog, params.M)
```

where I compute the same deterministic intermediate value `μi`

.