code: def _fwd_dist(power, fwd, vertices, source_ind, tidx=1):
      idx = np.argmax(power.data[:, tidx])
      rr_got = fwd["src"][0]["rr"][vertices[idx]]
      rr_want = fwd["src"][0]["rr"][vertices[source_ind]]
      return np.linalg.norm(rr_got - rr_want)
  
imports: import copy
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_equal, assert_array_less
  import mne
  from mne import pick_types
  from mne._fiff.constants import FIFF
  from mne._fiff.pick import pick_info
  from mne.beamformer import Beamformer, apply_dics, apply_dics_csd, apply_dics_epochs, apply_dics_tfr_epochs, make_dics, read_beamformer
  from mne.beamformer._compute_beamformer import _prepare_beamformer_input
  from mne.beamformer._dics import _prepare_noise_csd
  from mne.beamformer.tests.test_lcmv import _assert_weight_norm
  from mne.datasets import testing
  from mne.io import read_info
  from mne.proj import compute_proj_evoked, make_projector
  from mne.surface import _compute_nearest
  from mne.time_frequency import CrossSpectralDensity, EpochsTFR, csd_morlet, csd_tfr
  from mne.time_frequency.csd import _sym_mat_to_vector
  from mne.transforms import apply_trans, invert_transform
  from mne.utils import catch_logging, object_diff
label_desc: Use image node_id 6 for calling a global function with example usage: _fwd_dist(power, fwd, vertices, source_ind, tidx) and returns: np