code: def mf_data():
      """Produce Maxwell filtered data for beamforming."""
      raw = mne.io.read_raw_fif(fname_raw).fix_mag_coil_types()
      raw_sss = mne.preprocessing.maxwell_filter(raw)
      events = mne.find_events(raw_sss)
      del raw
      raw_sss.pick(picks="mag")
      assert len(raw_sss.ch_names) == 102
      epochs = mne.Epochs(raw_sss, events)
      data_cov = mne.compute_covariance(epochs, tmin=0)
      fwd = mne.read_forward_solution(fname_fwd)
      return epochs, data_cov, fwd
  
imports: from contextlib import nullcontext
  from copy import deepcopy
  from inspect import signature
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal, assert_array_less
  from scipy import linalg
  from scipy.spatial.distance import cdist
  import mne
  from mne import EvokedArray, VolSourceEstimate, VolVectorSourceEstimate, compute_rank, convert_forward_solution, pick_channels_cov, read_forward_solution, read_vectorview_selection
  from mne._fiff.compensator import set_current_comp
  from mne._fiff.constants import FIFF
  from mne.beamformer import Beamformer, apply_lcmv, apply_lcmv_cov, apply_lcmv_epochs, apply_lcmv_raw, make_dics, make_lcmv, read_beamformer
  from mne.beamformer._compute_beamformer import _prepare_beamformer_input
  from mne.datasets import testing
  from mne.minimum_norm import apply_inverse, make_inverse_operator
  from mne.minimum_norm.tests.test_inverse import _assert_free_ori_match
  from mne.simulation import simulate_evoked
  from mne.transforms import apply_trans, invert_transform
  from mne.utils import _record_warnings, catch_logging, object_diff
label_desc: Use image node_id 13 for calling a global function with example usage: mf_data() and returns: epochs,  data_cov,  fwd