code: def _cov_as_csd(cov, info):
      rng = np.random.RandomState(0)
      assert cov["data"].ndim == 2
      assert len(cov["data"]) == len(cov["names"])
      # we need to make this have at least some complex structure
      data = cov["data"] + 1e-1 * _rand_csd(rng, info)
      assert data.dtype == np.complex128
      return CrossSpectralDensity(
          _sym_mat_to_vector(data), cov["names"], 0.0, 16
      )
  
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 13 for calling a global function with example usage: _cov_as_csd(cov, info) and returns: CrossSpectralDensity