code: def test_regularize_cov():
      """Test cov regularization."""
      raw = read_raw_fif(raw_fname)
      raw.info["bads"].append(raw.ch_names[0])  # test with bad channels
      noise_cov = read_cov(cov_fname)
      # Regularize noise cov
      reg_noise_cov = regularize(
          noise_cov,
          raw.info,
          mag=0.1,
          grad=0.1,
          eeg=0.1,
          proj=True,
          exclude="bads",
          rank="full",
      )
      assert noise_cov["dim"] == reg_noise_cov["dim"]
      assert noise_cov["data"].shape == reg_noise_cov["data"].shape
      assert np.mean(noise_cov["data"] < reg_noise_cov["data"]) < 0.08
      # make sure all args are represented
      assert (
          set(_DATA_CH_TYPES_SPLIT)
          - set(signature(regularize).parameters)
          == set()
      )
  
imports: import itertools
  import sys
  from inspect import signature
  from pathlib import Path
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal, assert_equal
  from mne import Epochs, compute_covariance, compute_proj_raw, compute_rank, compute_raw_covariance, create_info, find_events, make_ad_hoc_cov, make_fixed_length_events, merge_events, pick_channels_cov, pick_info, pick_types, read_cov, read_evokeds, write_cov
  from mne._fiff.pick import _DATA_CH_TYPES_SPLIT
  from mne.channels import equalize_channels
  from mne.cov import _auto_low_rank_model, _regularized_covariance, compute_whitener, prepare_noise_cov, regularize, whiten_evoked
  from mne.datasets import testing
  from mne.fixes import _safe_svd
  from mne.io import RawArray, read_info, read_raw_ctf, read_raw_fif
  from mne.preprocessing import maxwell_filter
  from mne.rank import _compute_rank_int
  from mne.utils import _record_warnings, assert_snr, catch_logging
label_desc: Use image node_id 12 for calling a global function with example usage: test_regularize_cov() without return types