code: def test_depth_does_not_matter(
      bias_params_free, weight_norm, pick_ori
  ):
      """Test that depth weighting does not matter for normalized filters."""
      evoked, fwd, noise_cov, data_cov, _ = bias_params_free
      data = apply_lcmv(
          evoked,
          make_lcmv(
              evoked.info,
              fwd,
              data_cov,
              0.05,
              noise_cov,
              pick_ori=pick_ori,
              weight_norm=weight_norm,
              depth=0.0,
          ),
      ).data
      data_depth = apply_lcmv(
          evoked,
          make_lcmv(
              evoked.info,
              fwd,
              data_cov,
              0.05,
              noise_cov,
              pick_ori=pick_ori,
              weight_norm=weight_norm,
              depth=1.0,
          ),
      ).data
      assert data.shape == data_depth.shape
      for d1, d2 in zip(data, data_depth):
          # Sign flips can change when nearly orthogonal to the normal direction
          d2 *= np.sign(np.dot(d1.ravel(), d2.ravel()))
          atol = np.linalg.norm(d1) * 1e-7
          assert_allclose(d1, d2, atol=atol)
  
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 12 for calling a global function with example usage: test_depth_does_not_matter(bias_params_free, weight_norm, pick_ori) without return types