Visualize decision tree with not only training set tag distribution, but also test set tag distribution

We can visualize decision tree with training set distribution, for example

from matplotlib import pyplot as plt
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier 
from sklearn import tree

# Prepare the data data, can do row sample and column sample here
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Fit the classifier with default hyper-parameters
clf = DecisionTreeClassifier(random_state=1234)
clf.fit(X, y)


fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(clf, 
                   feature_names=iris.feature_names,  
                   class_names=iris.target_names,
                   filled=True)

gives us with distribution of training set, for example value = [50, 50, 50] in the root node.

However, I am not able to give it a test set, and get the distribution of test set in the visualized tree.