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Utils
```jsx
sudo apt-get install -y curl clang locate libdrm-dev silversearcher-ag htop
```

**CUDA**

cuda 11.8

```jsx

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module MiniOmega

using Cassette
import Base:~
export sample, unif, pointwise, <|, rt

const ID = NTuple{N, Int} where N

"Ω is a hypercube"
struct Ω

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using Omega

# Beta distributed random variables in [0, 1]
p1 = beta(0.5)
pgood = beta(0.5)
pbad = beta(0.5)

# Function that simulates the car
function simulate_car(ω)
  car_is_good = Bernoulli(ω, p1(ω), Bool)

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using Omega

# Add uncertainty to this if you want
region_size = 30_000 

# The number of groups
ngroups = ciid(ω -> Int(floor(uniform(ω, 1, region_size))))

# Produced random Boolean correlation matrix
function correlation_model(rng, nprobes = 10)

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using Omega

# Add uncertainty to this if you want
region_size = 30_000 

# The number of groups
ngroups = uniform(1:region_size)

# Produced random Boolean correlation matrix
function correlation_model(rng, nprobes = 10)

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abstract type Node end

struct Val <: Node
  x::Int
end

cons(::Val, x) = Val(x)
children(val::Val) = ()

struct Add{T1, T2} <: Node

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# Simplest Version

abstract type Node end

struct Val <: Node
  x::Int
end

children(val::Val) = ()

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using Omega
using Debugger
using Omega: mem

"Return sequence bounded between lb ad ub"
function f_(ω)
  lb = -1.0
  ub = 1.0
  xs = Float64[]
  x = rand(ω)

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module MiniOmega
using Random

# Ω Subtypes Random.AbstractRNG to be compatible with existing samplers

mutable struct Ω <: Random.AbstractRNG
  data::Dict{Int, Any}  # Map ids to random values
  i::Int                # Current id
  logscore::Float64
end

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using Arrows
using NamedTuples
import JLD: load
# import Arrows.BenchmarkArrows: STD_ROTATION_MATRIX, render
import Images: colorview, Gray

using NamedTuples
const STD_ROTATION_MATRIX = [0.94071758  -0.33430171 -0.05738258
                             -0.33835238 -0.91297877 -0.2280076
                             0.02383425  0.2339063 -0.97196698]