code: def test_ica_cov(method, cov, tmp_path, short_raw_epochs):
      """Test ICA with cov."""
      _skip_check_picard(method)
      raw, epochs, epochs_eog = short_raw_epochs
      if cov is not None:
          cov = read_cov(cov)
  
      # test reading and writing
      test_ica_fname = tmp_path / "test-ica.fif"
      kwargs = dict(n_pca_components=4)
  
      ica = ICA(
          noise_cov=cov, n_components=2, method=method, max_iter=1
      )
      with _record_warnings():  # ICA does not converge
          ica.fit(raw, picks=np.arange(10))
      _assert_ica_attributes(ica)
      sources = ica.get_sources(epochs).get_data(copy=False)
      assert ica.mixing_matrix_.shape == (2, 2)
      assert ica.unmixing_matrix_.shape == (2, 2)
      assert ica.pca_components_.shape == (10, 10)
      assert sources.shape[1] == ica.n_components_
  
      for exclude in [[], [0], np.array([1, 2, 3])]:
          ica.exclude = exclude
          ica.labels_ = {"foo": [0]}
          ica.save(test_ica_fname, overwrite=True)
          ica_read = read_ica(test_ica_fname)
          assert list(ica.exclude) == ica_read.exclude
          assert_equal(ica.labels_, ica_read.labels_)
          ica.apply(raw.copy(), **kwargs)
          ica.exclude = []
          ica.apply(raw.copy(), exclude=[1], **kwargs)
          assert ica.exclude == []
  
          ica.exclude = [0, 1]
          ica.apply(raw.copy(), exclude=[1], **kwargs)
          assert ica.exclude == [0, 1]
  
          ica_raw = ica.get_sources(raw)
          assert ica.exclude == [
              ica_raw.ch_names.index(e) for e in ica_raw.info["bads"]
          ]
  
imports: import os
  import shutil
  from contextlib import nullcontext
  from pathlib import Path
  import matplotlib.pyplot
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal, assert_equal
  from scipy import linalg, stats
  from scipy.io import loadmat, savemat
  from mne import Annotations, Epochs, EpochsArray, EvokedArray, Info, create_info, make_ad_hoc_cov, pick_channels_regexp, pick_types, read_events
  from mne._fiff.pick import _DATA_CH_TYPES_SPLIT, get_channel_type_constants
  from mne.cov import read_cov
  from mne.datasets import testing
  from mne.event import make_fixed_length_events
  from mne.io import RawArray, read_raw_ctf, read_raw_eeglab, read_raw_fif
  from mne.io.eeglab.eeglab import _check_load_mat
  from mne.preprocessing import ICA
  from mne.preprocessing import ica_find_ecg_events, ica_find_eog_events, read_ica
  from mne.preprocessing.ica import _ica_explained_variance, _sort_components, corrmap, get_score_funcs, read_ica_eeglab
  from mne.rank import _compute_rank_int
  from mne.utils import _record_warnings, catch_logging, check_version
label_desc: Use image node_id 16 for calling a global function with example usage: test_ica_cov(method, cov, tmp_path, short_raw_epochs) without return types