code: def test_dipole_fitting_ctf():
      """Test dipole fitting with CTF data."""
      pytest.importorskip("nibabel")
      raw_ctf = read_raw_ctf(fname_ctf).set_eeg_reference(
          projection=True
      )
      events = make_fixed_length_events(raw_ctf, 1)
      evoked = Epochs(raw_ctf, events, 1, 0, 0, baseline=None).average()
      cov = make_ad_hoc_cov(evoked.info)
      sphere = make_sphere_model((0.0, 0.0, 0.0))
      # XXX Eventually we should do some better checks about accuracy, but
      # for now our CTF phantom fitting tutorials will have to do
      # (otherwise we need to add that to the testing dataset, which is
      # a bit too big)
      fit_dipole(
          evoked,
          cov,
          sphere,
          rank=dict(meg=len(evoked.data)),
          tol=1e-3,
          accuracy="accurate",
      )
  
imports: import os
  import matplotlib.pyplot
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_equal, assert_array_less
  from mne import Dipole, DipoleFixed, Epochs, Evoked, EvokedArray, SourceEstimate, convert_forward_solution, fit_dipole, head_to_mni, make_ad_hoc_cov, make_fixed_length_events, make_forward_solution, make_sphere_model, pick_info, pick_types, read_cov, read_dipole, read_evokeds, read_forward_solution, read_source_spaces, transform_surface_to, write_evokeds
  from mne._fiff.constants import FIFF
  from mne.bem import _bem_find_surface, read_bem_solution
  from mne.datasets import testing
  from mne.dipole import _BDIP_ERROR_KEYS, get_phantom_dipoles
  from mne.io import read_raw_ctf, read_raw_fif
  from mne.proj import make_eeg_average_ref_proj
  from mne.simulation import simulate_evoked
  from mne.surface import _compute_nearest
  from mne.transforms import _get_trans, apply_trans
  from mne.utils import _record_warnings, requires_mne, run_subprocess
label_desc: Use image node_id 4 for calling a global function with example usage: test_dipole_fitting_ctf() without return types