code: def test_clean_t_r(rng):
      """Different TRs produce different results after butterworth filtering"""
      n_samples = 34
      # n_features  Must be higher than 500
      n_features = 501
      x_orig = generate_signals_plus_trends(
          n_features=n_features, n_samples=n_samples
      )
      random_tr_list1 = np.round(rng.uniform(size=3) * 10, decimals=2)
      random_tr_list2 = np.round(rng.uniform(size=3) * 10, decimals=2)
      for tr1, tr2 in zip(random_tr_list1, random_tr_list2):
          low_pass_freq_list = tr1 * np.array([1.0 / 100, 1.0 / 110])
          high_pass_freq_list = tr1 * np.array([1.0 / 210, 1.0 / 190])
          for low_cutoff, high_cutoff in zip(
              low_pass_freq_list, high_pass_freq_list
          ):
              det_one_tr = nisignal.clean(
                  x_orig,
                  t_r=tr1,
                  low_pass=low_cutoff,
                  high_pass=high_cutoff,
              )
              det_diff_tr = nisignal.clean(
                  x_orig,
                  t_r=tr2,
                  low_pass=low_cutoff,
                  high_pass=high_cutoff,
              )
  
              if not np.isclose(tr1, tr2, atol=0.3):
                  msg = (
                      "results do not differ "
                      f"for different TRs: {tr1} and {tr2} "
                      f"at cutoffs: low_pass={low_cutoff}, "
                      f"high_pass={high_cutoff} "
                      f"n_samples={n_samples}, n_features={n_features}"
                  )
                  np.testing.assert_(
                      np.any(np.not_equal(det_one_tr, det_diff_tr)), msg
                  )
                  del det_one_tr, det_diff_tr
  
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 10 for calling a global function with example usage: test_clean_t_r(rng) without return types