where is the maximum number of features indicated in the code below:

# Build a balanced logistic regression
clf_lr = LogisticRegression(class_weight='balanced', solver='liblinear', random_state=42)

# Build and fit a bagging classifier
clf_bag = BaggingClassifier(base_estimator=clf_lr, n_estimators=10, oob_score=True, random_state=500)
clf_bag.fit(X_train, y_train)

# Evaluate the accuracy on the test set and show the out-of-bag score
pred = clf_bag.predict(X_test)
print('Accuracy:  {:.2f}'.format(accuracy_score(y_test, pred)))
print('OOB-Score: {:.2f}'.format(clf_bag.oob_score_))

# Print the confusion matrix
print(confusion_matrix(y_test, pred))