code: def test_cortical_signal_suppression():
      """Test that CSS is dampening the cortical signal and has right shape."""
      ave = read_evokeds(fname_evoked)[0]
      eeg_ind = pick_types(ave.info, eeg=True)
      mag_ind = pick_types(ave.info, meg="mag")
      grad_ind = pick_types(ave.info, meg="grad")
      ave.data[mag_ind][0, :] = np.sin(
          2 * np.pi * 40 * ave.times
      ) * np.mean(np.abs(ave.data[mag_ind][0, :]))
      ave.data[mag_ind][1, :] = np.sin(
          2 * np.pi * 239 * ave.times
      ) * np.mean(np.abs(ave.data[mag_ind][1, :]))
      ave.data[grad_ind][0, :] = np.sin(
          2 * np.pi * 40 * ave.times
      ) * np.mean(np.abs(ave.data[grad_ind][0, :]))
      ave.data[eeg_ind][0, :] = np.sin(
          2 * np.pi * 40 * ave.times
      ) * np.mean(np.abs(ave.data[eeg_ind][0, :]))
      ave.data[eeg_ind][1, :] = np.sin(
          2 * np.pi * 239 * ave.times
      ) * np.mean(np.abs(ave.data[eeg_ind][1, :]))
      ave_f = cortical_signal_suppression(ave)
      cort_power = np.sum(np.abs(ave.data[eeg_ind][0, :]))
      deep_power = np.sum(np.abs(ave.data[eeg_ind][1, :]))
      cort_power_f = np.sum(np.abs(ave_f.data[eeg_ind][0, :]))
      deep_power_f = np.sum(np.abs(ave_f.data[eeg_ind][1, :]))
      rel_SNR_gain = (deep_power_f / deep_power) / (
          cort_power_f / cort_power
      )
      assert rel_SNR_gain > 0
      assert ave_f.data.shape == ave.data.shape
  
imports: import numpy
  from mne import pick_types, read_evokeds
  from mne.datasets import testing
  from mne.preprocessing._css import cortical_signal_suppression
label_desc: Use image node_id 1 for calling a global function with example usage: test_cortical_signal_suppression() without return types