code: def test_fit_and_transform(self):
      augmenter = FeatureAugmenter(
          column_value="val",
          column_id="id",
          column_sort="sort",
          column_kind="kind",
          kind_to_fc_parameters=self.kind_to_fc_parameters,
          n_jobs=0,
          disable_progressbar=True,
      )
  
      # Fit should do nothing
      returned_df = augmenter.fit()
      self.assertCountEqual(returned_df.__dict__, augmenter.__dict__)
      self.assertRaises(RuntimeError, augmenter.transform, None)
  
      augmenter.set_timeseries_container(self.test_df)
  
      # Add features to all time series
      X_with_index = pd.DataFrame(
          [{"feature_1": 1}] * 2, index=[10, 500]
      )
      X_transformed = augmenter.transform(X_with_index)
  
      # Require same shape
      for i in X_transformed.index:
          self.assertIn(i, X_with_index.index)
  
      for i in X_with_index.index:
          self.assertIn(i, X_transformed.index)
  
      self.assertEqual(X_transformed.shape, (2, 3))
  
      # Preserve old features
      self.assertCountEqual(
          list(X_transformed.columns),
          ["feature_1", "a__length", "b__length"],
      )
  
      # Features are not allowed to be NaN
      for index, row in X_transformed.iterrows():
          print((index, row))
          self.assertFalse(np.isnan(row["a__length"]))
          self.assertFalse(np.isnan(row["b__length"]))
  
file_path: tsfresh/tests/units/transformers/test_feature_augmenter.py
imports: import numpy
  import pandas
  from tests.fixtures import DataTestCase
  from tsfresh.feature_extraction.settings import ComprehensiveFCParameters
  from tsfresh.transformers import FeatureAugmenter
label_desc: Use image node_id 2 for calling the FeatureAugmenterTestCase obj's underlying member method code with example usage: obj.test_fit_and_transform() without return types