Is there any way to use a distribution from Distributions.jl with a custom RNG?

I know how to sample in a reproducible manner from a uniform distribution (see x1 and x2 in the code below).

But Distrubutions.jl seems to rely on Base.Random.GLOBAL_RNG which I do not want to use

I read this post, but was unable to solve my issue

I found a Julia implementation for Binomial (in LightGraphs), which I might just use, but I would prefer to have this working for any distribution.

```
using RandomNumbers, Distributions
const RNG_XOR=Xorshifts.Xorshift1024Plus(0x1234567890abcdef)
const RNG_MERSENNE=MersenneTwister(2001)
const RNG_SELECTED=RNG_MERSENNE
x1=rand(RNG_XOR) #this works fine, x1 is reproducible each time I initialize my seed & custom RNG, I get the same value for x1
x2=rand(RNG_MERSENNE) #this works fine as well
# this line throws an error (I was hoping this would solve my problem...)
# Base.Random.GLOBAL_RNG=RNG_SELECTED
some_distr=Binomial(200,0.1)
rand(some_distr) #this 'value' is only reproducible if i run srand(some_seed) beforehand, but I want to use a different RNG
```