code: def test_localization_bias_free(
      bias_params_free,
      reg,
      pick_ori,
      weight_norm,
      use_cov,
      depth,
      lower,
      upper,
      lower_ori,
      upper_ori,
  ):
      """Test localization bias for free-orientation LCMV."""
      evoked, fwd, noise_cov, data_cov, want = bias_params_free
      if not use_cov:
          evoked.pick(picks="grad")
          noise_cov = None
      with _record_warnings():  # rank deficiency of data_cov
          filters = make_lcmv(
              evoked.info,
              fwd,
              data_cov,
              reg,
              noise_cov,
              pick_ori=pick_ori,
              weight_norm=weight_norm,
              depth=depth,
          )
      loc = apply_lcmv(evoked, filters).data
      if pick_ori == "vector":
          ori = loc.copy() / np.linalg.norm(loc, axis=1, keepdims=True)
      else:
          # doesn't make sense for pooled (None) or max-power (can't be all 3)
          ori = None
      loc = (
          np.linalg.norm(loc, axis=1)
          if pick_ori == "vector"
          else np.abs(loc)
      )
      # Compute the percentage of sources for which there is no loc bias:
      max_idx = np.argmax(loc, axis=0)
      perc = (want == max_idx).mean() * 100
      assert lower <= perc <= upper
      _assert_free_ori_match(ori, max_idx, lower_ori, upper_ori)
  
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 10 for calling a global function with example usage: test_localization_bias_free(bias_params_free, reg, pick_ori, weight_norm, use_cov, depth, lower, upper, lower_ori, upper_ori) without return types