Hi, guys, I use bayesion optimization of python plus subfunction of julia to do my simulation, and I can successfully run the simple function see here, maybe I should ask another question, if I want to combine the speed of julia and the convenience of python, should I call julia sub-function(needs much time) from python or call python’s bayesian optimization packages from julia, I am in a mess…

```
# sub-function written in julia
function black_box_function(x, y) #save in the name of "example.jl"
return -x^2 - (y - 1)^2 + 1
end
```

```
# run in python
from bayes_opt import BayesianOptimization
from julia import Main
Main.include("example.jl")
Main.black_box_function(2,3) # it works
```

however if I put the sub-function into the bayesian optimization, then it crashed

```
# run in python
from bayes_opt import BayesianOptimization
from julia import Main
Main.include("example.jl")
# def black_box_function(x, y):
# return -x ** 2 - (y - 1) ** 2 + 1 # initial sub-function defined in python, and works well
# Bounded region of parameter space
pbounds = {'x': (2, 4), 'y': (-3, 3)}
optimizer = BayesianOptimization(
f=Main.black_box_function, # sub-function defined in julia, doesn't work
pbounds=pbounds,
random_state=1,
)
optimizer.maximize(
init_points=5,
n_iter=5,
)
```