code: def __init__(
      self,
      results_path=None,
      ml_task=None,
      features_count=None,
      n_jobs=-1,
  ):
      self._new_features = []
      self._new_columns = []
      self._ml_task = ml_task
      self._features_count = features_count
      self._n_jobs = n_jobs
      self._scorer = None
      if self._ml_task == BINARY_CLASSIFICATION:
          self._scorer = get_binary_score
      elif self._ml_task == MULTICLASS_CLASSIFICATION:
          self._scorer = get_multiclass_score
      else:
          self._scorer = get_regression_score
  
      self._error = None
  
      self._result_file = "golden_features.json"
      if results_path is not None:
          self._result_path = os.path.join(
              results_path, self._result_file
          )
  
          if os.path.exists(self._result_path):
              self.from_json(
                  json.load(open(self._result_path, "r")), results_path
              )
  
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 1 to create a new GoldenFeaturesTransformer object from inherited base classes: object with example: obj = GoldenFeaturesTransformer(results_path, ml_task, features_count, n_jobs)