code: def _stc_data_event(
      stc_counted, head_idx, sfreq, src=None, verts=None
  ):
      stc_idx, stc = stc_counted
      if isinstance(stc, (list, tuple)):
          if len(stc) != 2:
              raise ValueError(
                  "stc, if tuple, must be length 2, got %s"
                  % (len(stc),)
              )
          stc, stim_data = stc
      else:
          stim_data = None
      _validate_type(
          stc,
          _BaseSourceEstimate,
          "stc",
          "SourceEstimate or tuple with first entry SourceEstimate",
      )
      # Convert event data
      if stim_data is None:
          stim_data = np.zeros(len(stc.times), int)
          stim_data[np.argmin(np.abs(stc.times))] = head_idx
      del head_idx
      _validate_type(stim_data, np.ndarray, "stim_data")
      if stim_data.dtype.kind != "i":
          raise ValueError(
              "stim_data in a stc tuple must be an integer ndarray,"
              " got dtype %s" % (stim_data.dtype,)
          )
      if stim_data.shape != (len(stc.times),):
          raise ValueError(
              "event data had shape %s but needed to be (%s,) to"
              "match stc" % (stim_data.shape, len(stc.times))
          )
      # Validate STC
      if not np.allclose(sfreq, 1.0 / stc.tstep):
          raise ValueError(
              "stc and info must have same sample rate, "
              "got %s and %s" % (1.0 / stc.tstep, sfreq)
          )
      if len(stc.times) <= 2:  # to ensure event encoding works
          raise ValueError(
              "stc must have at least three time points, got %s"
              % (len(stc.times),)
          )
      verts_ = stc.vertices
      if verts is None:
          assert stc_idx == 0
      else:
          if len(verts) != len(verts_) or not all(
              np.array_equal(a, b) for a, b in zip(verts, verts_)
          ):
              raise RuntimeError(
                  "Vertex mismatch for stc[%d], "
                  "all stc.vertices must match" % (stc_idx,)
              )
      stc_data = stc.data
      if src is None:
          assert stc_idx == 0
      else:
          # on_missing depends on whether or not this is the first iteration
          on_missing = "warn" if verts is None else "ignore"
          _, stc_sel, _ = _stc_src_sel(src, stc, on_missing=on_missing)
          stc_data = stc_data[stc_sel]
      return stc_data, stim_data, verts_
  
imports: import os
  from collections.abc import Iterable
  from pathlib import Path
  import numpy
  from .._fiff.constants import FIFF
  from .._fiff.meas_info import Info
  from .._fiff.pick import pick_channels, pick_channels_forward, pick_info, pick_types
  from .._ola import _Interp2
  from ..bem import fit_sphere_to_headshape, make_sphere_model, read_bem_solution
  from ..chpi import _get_hpi_initial_fit, get_chpi_info, head_pos_to_trans_rot_t, read_head_pos
  from ..cov import Covariance, make_ad_hoc_cov, read_cov
  from ..event import _get_stim_channel
  from ..forward import _compute_forwards, _magnetic_dipole_field_vec, _merge_fwds, _prep_meg_channels, _prepare_for_forward, _stc_src_sel, _to_forward_dict, _transform_orig_meg_coils, convert_forward_solution, restrict_forward_to_stc
  from ..io import BaseRaw, RawArray
  from ..source_estimate import _BaseSourceEstimate
  from ..source_space._source_space import _ensure_src, _set_source_space_vertices, setup_volume_source_space
  from ..surface import _CheckInside
  from ..transforms import _get_trans, transform_surface_to
  from ..utils import _check_preload, _pl, _validate_type, check_random_state, logger, verbose
  from .source import SourceSimulator
label_desc: Use image node_id 10 for calling a global function with example usage: _stc_data_event(stc_counted, head_idx, sfreq, src, verts) and returns: stc_data,  stim_data,  verts_