code: def test_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,
      )
  
      df = pd.DataFrame(X, columns=[f"f{i}" for i in range(X.shape[1])])
  
      with tempfile.TemporaryDirectory() as tmpdir:
          FEATURES_COUNT = 42
          gft = GoldenFeaturesTransformer(
              tmpdir,
              "binary_classification",
              features_count=FEATURES_COUNT,
          )
          gft.fit(df, y)
  
          self.assertEqual(len(gft._new_features), FEATURES_COUNT)
  
          gft3 = GoldenFeaturesTransformer(
              tmpdir, "binary_classification"
          )
          gft3.from_json(gft.to_json(), tmpdir)
  
          df = gft3.transform(df)
          self.assertEqual(df.shape[1], N_COLS + FEATURES_COUNT)
  
imports: import unittest
  import tempfile
  import json
  import shutil
  import numpy
  import pandas
  from numpy.testing import assert_almost_equal
  from sklearn import datasets
  from supervised.preprocessing.goldenfeatures_transformer import GoldenFeaturesTransformer
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
label_desc: Use image node_id 8 for calling the GoldenFeaturesTransformerTest obj's underlying member method code with example usage: obj.test_features_count() without return types