code: def test_cov_estimation_on_raw_reg():
      """Test estimation from raw with regularization."""
      pytest.importorskip("sklearn")
      raw = read_raw_fif(raw_fname, preload=True)
      with raw.info._unlock():
          raw.info["sfreq"] /= 10.0
      raw = RawArray(
          raw._data[:, ::10].copy(), raw.info
      )  # decimate for speed
      cov_mne = read_cov(erm_cov_fname)
      with pytest.warns(RuntimeWarning, match="Too few samples"):
          # "diagonal_fixed" is much faster. Use long epochs for speed.
          cov = compute_raw_covariance(
              raw, tstep=5.0, method="diagonal_fixed"
          )
      assert_snr(cov.data, cov_mne.data, 5)
  
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 8 for calling a global function with example usage: test_cov_estimation_on_raw_reg() without return types