code: def compute_bias(raw):
      events = find_events(raw, "STI201", verbose=False)
      events = events[1:]  # first one has an artifact
      tmin, tmax = -0.2, 0.1
      epochs = mne.Epochs(
          raw,
          events,
          dipole_number,
          tmin,
          tmax,
          baseline=(None, -0.01),
          preload=True,
          verbose=False,
      )
      sphere = mne.make_sphere_model(
          r0=(0.0, 0.0, 0.0), head_radius=None, verbose=False
      )
      cov = mne.compute_covariance(
          epochs, tmax=0, method="oas", rank=None, verbose=False
      )
      idx = epochs.time_as_index(0.036)[0]
      data = epochs.get_data(copy=False)[:, :, idx].T
      evoked = mne.EvokedArray(data, epochs.info, tmin=0.0)
      dip = fit_dipole(evoked, cov, sphere, n_jobs=None, verbose=False)[
          0
      ]
      actual_pos = mne.dipole.get_phantom_dipoles()[0][
          dipole_number - 1
      ]
      misses = 1000 * np.linalg.norm(dip.pos - actual_pos, axis=-1)
      return misses
  
imports: import numpy
  import mne
  from mne import find_events, fit_dipole
  from mne.datasets.brainstorm import bst_phantom_elekta
  from mne.io import read_raw_fif
label_desc: Use image node_id 1 for calling a global function with example usage: compute_bias(raw) and returns: misses