code: def _read_annotations_eeglab(eeg, uint16_codec=None):
      r"""Create Annotations from EEGLAB file.
  
      This function reads the event attribute from the EEGLAB
      structure and makes an :class:`mne.Annotations` object.
  
      Parameters
      ----------
      eeg : object | str
          'EEG' struct or the path to the (EEGLAB) .set file.
      uint16_codec : str | None
          If your \*.set file contains non-ascii characters, sometimes reading
          it may fail and give rise to error message stating that "buffer is
          too small". ``uint16_codec`` allows to specify what codec (for example:
          'latin1' or 'utf-8') should be used when reading character arrays and
          can therefore help you solve this problem.
  
      Returns
      -------
      annotations : instance of Annotations
          The annotations present in the file.
      """
      if isinstance(eeg, str):
          eeg = _check_load_mat(eeg, uint16_codec=uint16_codec)
  
      if not hasattr(eeg, "event"):
          events = []
      elif (
          isinstance(eeg.event, dict)
          and np.array(eeg.event["latency"]).ndim > 0
      ):
          events = _dol_to_lod(eeg.event)
      elif not isinstance(eeg.event, (np.ndarray, list)):
          events = [eeg.event]
      else:
          events = eeg.event
      events = _bunchify(events)
      description = [str(event.type) for event in events]
      onset = [event.latency - 1 for event in events]
      duration = np.zeros(len(onset))
      if len(events) > 0 and hasattr(events[0], "duration"):
          for idx, event in enumerate(events):
              # empty duration fields are read as empty arrays
              is_empty_array = (
                  isinstance(event.duration, np.ndarray)
                  and len(event.duration) == 0
              )
              duration[idx] = (
                  np.nan if is_empty_array else event.duration
              )
  
      return Annotations(
          onset=np.array(onset) / eeg.srate,
          duration=duration / eeg.srate,
          description=description,
          orig_time=None,
      )
  
imports: import os.path
  from os import PathLike
  from pathlib import Path
  import numpy
  from mne.utils.check import _check_option
  from ..._fiff._digitization import _ensure_fiducials_head
  from ..._fiff.constants import FIFF
  from ..._fiff.meas_info import create_info
  from ..._fiff.pick import _PICK_TYPES_KEYS
  from ..._fiff.utils import _find_channels, _read_segments_file
  from ...annotations import Annotations, read_annotations
  from ...channels import make_dig_montage
  from ...defaults import DEFAULTS
  from ...epochs import BaseEpochs
  from ...event import read_events
  from ...utils import Bunch, _check_fname, _check_head_radius, fill_doc, logger, verbose, warn
  from ..base import BaseRaw
  from ._eeglab import _readmat
label_desc: Use image node_id 14 for calling a global function with example usage: _read_annotations_eeglab(eeg, uint16_codec) and returns: Annotations