Python decorators (functools etc) in Alteryx Python Tool?

Question

Does anybody know if there is a way to make use of python decorators like functools (@cache) for Python tool in Alteryx? Looking to implement a repetitive function evaluation in python and caching the results would save enormously.

I have seen use cases for something like below in Python, but I wonder how would you achieve similar performance in Alteryx using Python.

from functools import lru_cache
import numpy as np


def estimate_error(x):
    t = 1 / (1 + 0.33 * abs(x))
    pre_erf = (((1.06 * t - 1.45) * t + 1.42) * t - 0.29) * t + 0.25
    erf = np.sign(x) * (1 - pre_erf * t * np.exp(- x * x))
    return erf


@lru_cache(maxsize=1024)
def fast_ln_sf(x, scale, shape):
    return 0.5 - 0.5 * estimate_error(0.7 * (np.log(x) - np.log(scale)) / shape)

I intend to use this function as an objective for my optimization problem, meaning this function gets evaluated at least 1000s of times if I have high number of variables.

The use case for this function is of the type:

def obj(x, *args):
    a = args[0].to_numpy()
    s = args[1].to_numpy()
    c = args[2].to_numpy()
    n = args[3]

    sum_ = 0
    for i in range(n):
    sum _ += (a * fast_ln_sf(x[i], s, c))
    return -1 * sum_


solution = scipy.optimize.minimize(obj, args = [df.a, df.b, df.s, df.c, df.shape[0]])