code: def test_find_overlaps():
      """Test finding overlapping occular events between the left and right eyes.
  
      In the simulated blink df below, the first two rows
      will be considered an overlap because the diff() of both the 'time' and
      'end_time' values is <.05 (50ms). the 3rd and 4th rows will not be
      considered an overlap because the diff() of the 'time' values is > .05
      (4.20 - 4.14 = .06). The 5th and 6th rows will not be considered an
      overlap because they are both left eye events.
      """
      blink_df = pd.DataFrame(
          {
              "eye": ["L", "R", "L", "R", "L", "L"],
              "time": [0.01, 0.04, 4.14, 4.20, 6.50, 6.504],
              "end_time": [0.05, 0.08, 4.18, 4.22, 6.60, 6.604],
          }
      )
      overlap_df = _find_overlaps(blink_df)
      assert (
          len(overlap_df["eye"].unique()) == 3
      )  # ['both', 'left', 'right']
      assert len(overlap_df) == 5  # ['both', 'L', 'R', 'L', 'L']
      assert overlap_df["eye"].iloc[0] == "both"
  
file_path: mne-python/mne/io/eyelink/tests/test_eyelink.py
imports: from pathlib import Path
  import numpy
  import pytest
  from numpy.testing import assert_allclose
  from mne._fiff.constants import FIFF
  from mne._fiff.pick import _DATA_CH_TYPES_SPLIT
  from mne.datasets.testing import data_path, requires_testing_data
  from mne.io import read_raw_eyelink
  from mne.io.eyelink._utils import _adjust_times, _find_overlaps
  from mne.io.tests.test_raw import _test_raw_reader
label_desc: Use image node_id 5 for calling a global function with example usage: test_find_overlaps() without return types