code: def test_triangle_neighbors():
      """Test efficient vertex neighboring triangles for surfaces."""
      this = read_source_spaces(fname)[0]
      this["neighbor_tri"] = [list() for _ in range(this["np"])]
      for p in range(this["ntri"]):
          verts = this["tris"][p]
          this["neighbor_tri"][verts[0]].append(p)
          this["neighbor_tri"][verts[1]].append(p)
          this["neighbor_tri"][verts[2]].append(p)
      this["neighbor_tri"] = [
          np.array(nb, int) for nb in this["neighbor_tri"]
      ]
  
      neighbor_tri = _triangle_neighbors(this["tris"], this["np"])
      assert all(
          np.array_equal(nt1, nt2)
          for nt1, nt2 in zip(neighbor_tri, this["neighbor_tri"])
      )
  
imports: from pathlib import Path
  from shutil import copytree
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_equal, assert_array_less, assert_equal
  import mne
  from mne import SourceEstimate, add_source_space_distances, compute_source_morph, get_volume_labels_from_src, make_sphere_model, morph_source_spaces, pick_types, read_bem_solution, read_bem_surfaces, read_freesurfer_lut, read_source_spaces, read_trans, setup_source_space, setup_volume_source_space, write_source_spaces
  from mne._fiff.constants import FIFF
  from mne._fiff.pick import _picks_to_idx
  from mne.datasets import testing
  from mne.fixes import _get_img_fdata
  from mne.source_estimate import _get_src_type
  from mne.source_space import compute_distance_to_sensors, get_decimated_surfaces
  from mne.source_space._source_space import _compare_source_spaces
  from mne.surface import _accumulate_normals, _triangle_neighbors
  from mne.utils import _record_warnings, requires_mne, run_subprocess
label_desc: Use image node_id 10 for calling a global function with example usage: test_triangle_neighbors() without return types