code: def transform(self, X):
      for new_feature in self._new_features:
          new_col = "_".join(
              [
                  new_feature["feature1"],
                  new_feature["operation"],
                  new_feature["feature2"],
              ]
          )
          if new_feature["operation"] == "diff":
              X[new_col] = (
                  X[new_feature["feature1"]]
                  - X[new_feature["feature2"]]
              )
          elif new_feature["operation"] == "ratio":
              a, b = (
                  np.array(X[new_feature["feature1"]], dtype=float),
                  np.array(X[new_feature["feature2"]], dtype=float),
              )
              X[new_col] = np.divide(
                  a, b, out=np.zeros_like(a), where=b != 0
              ).reshape(-1, 1)
          elif new_feature["operation"] == "sum":
              X[new_col] = (
                  X[new_feature["feature1"]]
                  + X[new_feature["feature2"]]
              )
          elif new_feature["operation"] == "multiply":
              X[new_col] = (
                  X[new_feature["feature1"]]
                  * X[new_feature["feature2"]]
              )
  
      return X
  
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 3 for calling the GoldenFeaturesTransformer obj's underlying member method code with example usage: obj.transform(X) and returns: X