code: def test_golden_features_count(self):
      N_COLS = 10
      X, y = datasets.make_classification(
          n_samples=100,
          n_features=N_COLS,
          n_informative=6,
          n_redundant=1,
          n_classes=2,
          n_clusters_per_class=3,
          n_repeated=0,
          shuffle=False,
          random_state=0,
      )
  
      X = pd.DataFrame(X, columns=[f"f{i}" for i in range(X.shape[1])])
  
      automl = AutoML(
          results_path=self.automl_dir,
          total_time_limit=50,
          algorithms=["Xgboost"],
          train_ensemble=False,
          golden_features=50,
          explain_level=0,
          start_random_models=1,
      )
      automl.fit(X, y)
  
      self.assertEqual(len(automl._models), 2)
  
      # there should be 50 golden features
      with open(
          os.path.join(self.automl_dir, "golden_features.json")
      ) as fin:
          d = json.loads(fin.read())
          self.assertEqual(len(d["new_features"]), 50)
  
file_path: mljar-supervised/tests/tests_automl/test_golden_features.py
imports: import os
  import unittest
  import tempfile
  import json
  import numpy
  import pandas
  import shutil
  from supervised import AutoML
  from numpy.testing import assert_almost_equal
  from sklearn import datasets
  from supervised.exceptions import AutoMLException
label_desc: Use image node_id 4 for calling the AutoMLGoldenFeaturesTest obj's underlying member method code with example usage: obj.test_golden_features_count() without return types