code: def test_clean_zscore(rng):
      n_samples = 500
      n_features = 5
  
      signals, _, _ = generate_signals(
          n_features=n_features, length=n_samples
      )
  
      signals += rng.standard_normal(size=(1, n_features))
      cleaned_signals_ = clean(signals, standardize="zscore")
      np.testing.assert_almost_equal(cleaned_signals_.mean(0), 0)
      np.testing.assert_almost_equal(cleaned_signals_.std(0), 1)
  
      # Repeating test above but for new correct strategy
      cleaned_signals = clean(signals, standardize="zscore_sample")
      np.testing.assert_almost_equal(cleaned_signals.mean(0), 0)
      np.testing.assert_almost_equal(
          cleaned_signals.std(0), 1, decimal=3
      )
  
      # Show outcome from two zscore strategies is not equal
      with pytest.raises(AssertionError):
          np.testing.assert_array_equal(
              cleaned_signals_, cleaned_signals
          )
  
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 19 for calling a global function with example usage: test_clean_zscore(rng) without return types