code: def get_regression_score(X_train, y_train, X_test, y_test):
      clf = DecisionTreeRegressor(max_depth=3)
      clf.fit(X_train, y_train)
      pred = clf.predict(X_test)
      ll = mean_squared_error(y_test, pred)
      return ll
  
imports: import datetime
  import itertools
  import json
  import os
  import time
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from sklearn.metrics import log_loss, mean_squared_error
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.exceptions import AutoMLException
  from supervised.utils.jsonencoder import MLJSONEncoder
label_desc: Use image node_id 2 for calling a global function with example usage: get_regression_score(X_train, y_train, X_test, y_test) and returns: ll