code: def test_tsfresh_combiner_features(dummy_data):
      from tsfresh.feature_extraction.feature_calculators import (
          index_mass_quantile,
          linear_trend,
          linear_trend_timewise,
          spkt_welch_density,
      )
  
      combiner_feats = MultipleFeatureDescriptors(
          functions=[
              tsfresh_combiner_wrapper(
                  index_mass_quantile,
                  param=[{"q": v} for v in [0.15, 0.5, 0.75]],
              ),
              tsfresh_combiner_wrapper(
                  linear_trend,
                  param=[
                      {"attr": v}
                      for v in ["intercept", "slope", "stderr"]
                  ],
              ),
              tsfresh_combiner_wrapper(
                  spkt_welch_density,
                  param=[{"coeff": v} for v in range(5)],
              ),
              # This function requires a pd.Series with a pd.DatetimeIndex
              tsfresh_combiner_wrapper(
                  linear_trend_timewise,
                  param=[{"attr": v} for v in ["intercept", "slope"]],
              ),
          ],
          series_names=["ACC_x", "EDA"],
          windows="5min",
          strides="5min",
      )
      feature_collection = FeatureCollection(combiner_feats)
  
      res_df = feature_collection.calculate(
          dummy_data, return_df=True, n_jobs=0
      )
      assert res_df.shape[1] == (3 + 3 + 5 + 2) * 2
      assert res_df.shape[0] > 0
      assert not res_df.isna().any().any()
  
file_path: tsflex/tests/test_features_integration.py
imports: import sys
  import numpy
  import pytest
  import seglearn
  from tsflex.features import FeatureCollection, FuncWrapper, MultipleFeatureDescriptors
  from tsflex.features.integrations import catch22_wrapper, seglearn_feature_dict_wrapper, seglearn_wrapper, tsfel_feature_dict_wrapper, tsfresh_combiner_wrapper, tsfresh_settings_wrapper
  from .utils import dummy_data
label_desc: Use image node_id 4 for calling a global function with example usage: test_tsfresh_combiner_features(dummy_data) without return types