How to plot heatmap for AUC score across parameters?

While performing GridSearch CV on a decision tree model I gave the values for min_samples_split as [10,100,200] and max_depth as [10,20,40], and then I collected the AUC scores.
Now I want to plot a heatmap such that the x-axis should be indicating the min_samples_split and y-axis should be max_depth and the cell values should signify AUC scores that the model calculated.

But I'm unable to combine these three data: min_samples_split, max_depth, and AUC score. Please refer to the code below:

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
param={'max_depth':[10,20,40],
      'min_samples_split':[10,100,200]}
decision_tree=DecisionTreeClassifier(class_weight='balanced')
clf=GridSearchCV(decision_tree,param,cv=3,scoring='roc_auc',return_train_score=True,n_jobs=-1)
clf.fit(X_train_setOne,Y_train)

and then I'm finding out the AUC scores:

train_AUC=clf.cv_results_['mean_train_score']
train_AUC_std=clf.cv_results_['std_train_score']
CV_AUC=clf.cv_results_['mean_test_score']
CV_AUC_std=clf.cv_results_['std_test_score']

Now I want to plot a heatmap.