code: def test_mxne_inverse_empty():
      """Tests solver with too high alpha."""
      evoked = read_evokeds(fname_data, condition=0, baseline=(None, 0))
      evoked.pick("grad", exclude="bads")
      fname_fwd = (
          data_path
          / "MEG"
          / "sample"
          / "sample_audvis_trunc-meg-eeg-oct-4-fwd.fif"
      )
      forward = mne.read_forward_solution(fname_fwd)
      forward = mne.pick_types_forward(
          forward, meg="grad", eeg=False, exclude=evoked.info["bads"]
      )
      cov = read_cov(fname_cov)
      with pytest.warns(RuntimeWarning, match="too big"):
          stc, residual = mixed_norm(
              evoked,
              forward,
              cov,
              n_mxne_iter=3,
              alpha=99,
              return_residual=True,
          )
          assert stc.data.size == 0
          assert stc.vertices[0].size == 0
          assert stc.vertices[1].size == 0
          assert_allclose(evoked.data, residual.data)
  
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal, assert_array_less
  import mne
  from mne import convert_forward_solution, read_cov, read_evokeds, read_forward_solution
  from mne.datasets import testing
  from mne.dipole import Dipole
  from mne.inverse_sparse import mixed_norm, tf_mixed_norm
  from mne.inverse_sparse.mxne_inverse import _compute_mxne_sure, _split_gof, make_stc_from_dipoles
  from mne.inverse_sparse.mxne_optim import norm_l2inf
  from mne.label import read_label
  from mne.minimum_norm import apply_inverse, make_inverse_operator
  from mne.minimum_norm.tests.test_inverse import assert_stc_res, assert_var_exp_log
  from mne.simulation import simulate_evoked, simulate_sparse_stc
  from mne.source_estimate import VolSourceEstimate
  from mne.utils import _record_warnings, assert_stcs_equal, catch_logging
label_desc: Use image node_id 8 for calling a global function with example usage: test_mxne_inverse_empty() without return types