Are predictions on scikit-learn models thread-safe?

Given some classifier (SVC/Forest/NN/whatever) is it safe to call .predict on the same instance concurrently from different threads?

From a distant point of view, my guess is they do not mutate any internal state. But I did not find anything in the docs about it.

Here is a minimal example showing what I mean:

#!/usr/bin/env python3
import threading

from sklearn import datasets
from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier

X, y = datasets.load_iris(return_X_y=True)

# Some model. Might be any type, e.g.:
clf = svm.SVC()
clf = RandomForestClassifier(),
clf = MLPClassifier(solver='lbfgs')

clf.fit(X, y)


def use_model_for_predictions():
    for _ in range(10000):
        clf.predict(X[0:1])


# Is this safe?
thread_1 = threading.Thread(target=use_model_for_predictions)
thread_2 = threading.Thread(target=use_model_for_predictions)
thread_1.start()
thread_2.start()

from Are predictions on scikit-learn models thread-safe?