I am trying to use a Metropolis adjusted Langevin sampler, from `AdvancedMH.MALA`

. I did a simple test to see if things are working correctly, which is to sample from a multivariate normal distribution. Here is the code:

```
import AdvancedMH, LogDensityProblems, Distributions
struct TheNormalLogDensity{M}
A::M
end
# can do gradient
LogDensityProblems.capabilities(::Type{<:TheNormalLogDensity}) = LogDensityProblems.LogDensityOrder{1}()
LogDensityProblems.dimension(d::TheNormalLogDensity) = size(d.A, 1)
LogDensityProblems.logdensity(d::TheNormalLogDensity, x) = -x' * d.A * x / 2
function LogDensityProblems.logdensity_and_gradient(d::TheNormalLogDensity, x)
return -x' * d.A * x / 2, -d.A * x
end
Σ = [1.5 0.35; 0.35 1.0]
σ² = 0.01
spl = AdvancedMH.MALA(g -> Distributions.MvNormal((σ² / 2) .* g, σ² * I))
chain = AdvancedMH.sample(TheNormalLogDensity(inv(Σ)), spl, 500000; initial_params=ones(2))
data = stack([c.params for c = chain])
```

I can then compute an empirical covariance matrix from the sampled data, which should be close to `Σ`

because we have a lot of samples here.

```
# this should be close to Σ
C = data * data' / size(data, 2)
```

But actually, I get an empirical covariance that seems very different from `Σ`

:

```
> data * data' / size(data, 2)
2×2 Matrix{Float64}:
0.507211 0.124855
0.124855 0.341631
```

Am I doing something wrong here?