How to pre warm-up Numba's JIT?

Question

I have been struggling with Numba in the sense that whenever I write a function for it, it has a very long warm-up for the first time I use it. I wanted to ask is there a way to prewarm up the JIT function?

For example if I write this function y=1/(log(x+0.1))^2 as a Numba function:

@jit(parallel=True,error_model='numpy')
def f_numba(x_vec): 
    N=len(x_vec)
    res=np.empty(N)
    for i in prange(N):
        x=x_vec[i]
        x=np.log(x+0.1)
        res[i]=1/(x*x)
    return res

I used this array to test the speed of the function:

N=150000
x_vect=np.random.rand(N)

And for measuring the execution time of the function I used this:

for i in range(5):
    start=timer()
    f_numba(x_vect)
    print('#',timer()-start)

It takes 0.8 seconds for the first run and 0.001 seconds for all subsequent runs. If I could somehow pre-warm-up the JIT function to avoid this latency, it would be great. I tried using the first run with a dummy array with a small size using x_warm=np.random.rand(10) and then running f_numba(x_warm) but the warm-up time didn't change at all. Any suggestions?

For completionism, here are the libraries called:

import numpy as np
from timeit import default_timer as timer
from numba import jit, prange

I'm using Jupyter Notebook with Python 3.7.