code: def get_score(item):
      col1 = item[0]
      col2 = item[1]
      X_train = item[2]
      y_train = item[3]
      X_test = item[4]
      y_test = item[5]
      scorer = item[6]
  
      try:
          x_train = np.array(X_train[col1] - X_train[col2]).reshape(
              -1, 1
          )
          x_test = np.array(X_test[col1] - X_test[col2]).reshape(-1, 1)
          diff_score = scorer(x_train, y_train, x_test, y_test)
      except Exception as e:
          diff_score = None
          print(str(e))
  
      try:
          a, b = (
              np.array(X_train[col1], dtype=float),
              np.array(X_train[col2], dtype=float),
          )
          x_train = np.divide(
              a, b, out=np.zeros_like(a), where=b != 0
          ).reshape(-1, 1)
          a, b = np.array(X_test[col1], dtype=float), np.array(
              X_test[col2], dtype=float
          )
          x_test = np.divide(
              a, b, out=np.zeros_like(a), where=b != 0
          ).reshape(-1, 1)
          ratio_1_score = scorer(x_train, y_train, x_test, y_test)
      except Exception as e:
          print(str(e))
          ratio_1_score = None
  
      try:
          b, a = (
              np.array(X_train[col1], dtype=float),
              np.array(X_train[col2], dtype=float),
          )
          x_train = np.divide(
              a, b, out=np.zeros_like(a), where=b != 0
          ).reshape(-1, 1)
          b, a = np.array(X_test[col1], dtype=float), np.array(
              X_test[col2], dtype=float
          )
          x_test = np.divide(
              a, b, out=np.zeros_like(a), where=b != 0
          ).reshape(-1, 1)
          ratio_2_score = scorer(x_train, y_train, x_test, y_test)
      except Exception as e:
          print(str(e))
          ratio_2_score = None
  
      try:
          x_train = np.array(X_train[col1] + X_train[col2]).reshape(
              -1, 1
          )
          x_test = np.array(X_test[col1] + X_test[col2]).reshape(-1, 1)
          sum_score = scorer(x_train, y_train, x_test, y_test)
      except Exception as e:
          sum_score = None
          print(str(e))
  
      try:
          x_train = np.array(X_train[col1] * X_train[col2]).reshape(
              -1, 1
          )
          x_test = np.array(X_test[col1] * X_test[col2]).reshape(-1, 1)
          multiply_score = scorer(x_train, y_train, x_test, y_test)
      except Exception as e:
          multiply_score = None
          print(str(e))
  
      return (
          diff_score,
          ratio_1_score,
          ratio_2_score,
          sum_score,
          multiply_score,
      )
  
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 4 for calling a global function with example usage: get_score(item) and returns: diff_score,  ratio_1_score,  ratio_2_score,  sum_score,  multiply_score