code: def test_clean_kwargs():
      """Providing kwargs to clean should change the filtered results."""
      n_samples = 34
      n_features = 501
      x_orig = generate_signals_plus_trends(
          n_features=n_features, n_samples=n_samples
      )
      kwargs = [
          {
              "butterworth__padtype": "even",
              "butterworth__padlen": 10,
              "butterworth__order": 3,
          },
          {
              "butterworth__padtype": None,
              "butterworth__padlen": None,
              "butterworth__order": 1,
          },
          {
              "butterworth__padtype": "constant",
              "butterworth__padlen": 20,
              "butterworth__order": 10,
          },
      ]
      # Base result
      t_r, high_pass, low_pass = 0.8, 0.01, 0.08
      base_filtered = nisignal.clean(
          x_orig, t_r=t_r, low_pass=low_pass, high_pass=high_pass
      )
      for kwarg_set in kwargs:
          test_filtered = nisignal.clean(
              x_orig,
              t_r=t_r,
              low_pass=low_pass,
              high_pass=high_pass,
              **kwarg_set,
          )
          # Check that results are **not** the same.
          np.testing.assert_(
              np.any(np.not_equal(base_filtered, test_filtered))
          )
  
imports: import os.path
  import numpy
  import pytest
  import scipy.signal
  from pandas import read_csv
  from nilearn import signal
  from nilearn.conftest import _rng
  from nilearn.signal import clean
label_desc: Use image node_id 11 for calling a global function with example usage: test_clean_kwargs() without return types