Make a random forest estimator the exact same of a decision tree

The idea is to make one of the trees of a Random Forest, to be built exactly equal to a Decision Tree.

First, we load all libraries, fit a decision tree and plot it.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
import random
from pprint import pprint
import pdb
random.seed(0)
np.random.seed(0)
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier

data = load_iris()

dtc = DecisionTreeClassifier(random_state=0)
dtc.fit(data['data'].squeeze(),data.target)

tree.plot_tree(dtc)

We then do the same thing with the random forest

rf  = RandomForestClassifier(n_estimators=1,max_features=None,random_state=0)
rf.fit(data['data'].squeeze(),data.target)
tree.plot_tree(rf.estimators_[0])

My question:

Is it possible to make the exact same the first tree of the random forest and a decision tree?