code to find best split based on standard error

find_split

xi = x # initialization of input
yi = y # initialization of target
# x,y --> use where no need to change original y
ei = 0 # initialization of error
n = len(yi)  # number of rows
predf = 0 # initial prediction 0

for i in range(30): # loop will make 30 trees (n_estimators). 
    tree = DecisionTree(xi,yi) # DecisionTree scratch code can be found in shared github/kaggle link. 
                               # It just create a single decision tree with provided min. sample leaf
    tree.find_better_split(0)  # For selected input variable, this splits (<n and >n) data so that std. deviation of 
                               # target variable in both splits is minimum as compared to all other splits
    
    r = np.where(xi == tree.split)[0][0]   #  finds index where this best split occurs
    
    left_idx = np.where(xi <= tree.split)[0] # index lhs of split
    right_idx = np.where(xi > tree.split)[0] # index rhs of split

Which decion tree library is used.I am confused