I just started playing with Turing so this is probably a stupid question, is there an easy way (say for the purposes of inspecting my model) to get a posterior gradient of a latent space variable at fixed data? Here’s my attempt, which doesn’t seem to work with either Zygote or ForwardDiff:

```
@model function MyModel(x)
σ ~ Uniform(0, 1)
x ~ Normal(0, σ^2)
return x
end
model = MyModel(missing)
Zygote.gradient(σ -> logprob"x=1 | model=model, σ=σ", 1)
ForwardDiff.derivative(σ -> logprob"x=1 | model=model, σ=σ", 1)
```

Both last two lines error (I’m guessing this is not the right way to do this.)