code: def apply_inverse_cov(
      cov,
      info,
      inverse_operator,
      nave=1,
      lambda2=1 / 9,
      method="dSPM",
      pick_ori=None,
      prepared=False,
      label=None,
      method_params=None,
      use_cps=True,
      verbose=None,
  ):
      """Apply inverse operator to covariance data.
  
      Parameters
      ----------
      cov : instance of Covariance
          Covariance data, computed on the time segment for which to compute
          source power.
      %(info_not_none)s Used specify the channels to include.
      inverse_operator : instance of InverseOperator
          Inverse operator.
      nave : int
          Number of averages used to regularize the solution.
      lambda2 : float
          The regularization parameter.
      method : "MNE" | "dSPM" | "sLORETA" | "eLORETA"
          Use minimum norm, dSPM (default), sLORETA, or eLORETA.
      %(pick_ori_novec)s
      prepared : bool
          If True, do not call :func:`prepare_inverse_operator`.
      label : Label | None
          Restricts the source estimates to a given label. If None,
          source estimates will be computed for the entire source space.
      method_params : dict | None
          Additional options for eLORETA. See Notes for details.
      %(use_cps)s
      %(verbose)s
  
      Returns
      -------
      stc : SourceEstimate | VectorSourceEstimate | VolSourceEstimate
          The source estimates.
  
      See Also
      --------
      apply_inverse : Apply inverse operator to evoked object.
      apply_inverse_raw : Apply inverse operator to raw object.
      apply_inverse_epochs : Apply inverse operator to epochs object.
      apply_inverse_tfr_epochs : Apply inverse operator to epochs tfr object.
  
      Notes
      -----
      .. versionadded:: 0.20
  
      This code is based on the original research code from
      :footcite:`Sabbagh2020` and has been useful to correct for individual field
      spread using source localization in the context of predictive modeling.
  
      References
      ----------
      .. footbibliography::
      """
      _validate_type(cov, Covariance, cov)
      _validate_type(
          inverse_operator, InverseOperator, "inverse_operator"
      )
      sel = _pick_channels_inverse_operator(
          cov["names"], inverse_operator
      )
      use_names = [cov["names"][idx] for idx in sel]
      info = pick_info(
          info, pick_channels(info["ch_names"], use_names, ordered=True)
      )
      evoked = EvokedArray(
          np.eye(len(info["ch_names"])), info, nave=nave, comment="cov"
      )
      is_free_ori = (
          inverse_operator["source_ori"] == FIFF.FIFFV_MNE_FREE_ORI
      )
      _check_option("pick_ori", pick_ori, (None, "normal"))
      if is_free_ori and pick_ori is None:
          use_ori = "vector"
          combine = True
      else:
          use_ori = pick_ori
          combine = False
      stc = _apply_inverse(
          evoked,
          inverse_operator,
          lambda2,
          method,
          use_ori,
          prepared,
          label,
          method_params,
          return_residual=False,
          use_cps=use_cps,
      )
      # apply (potentially rotated in the vector case) operator twice
      K = np.reshape(stc.data, (-1, stc.data.shape[-1]))
      # diagonal entries of A @ B are given by (A * B.T).sum(axis=1), so this is
      # equivalent to np.diag(K @ cov.data[sel][:, sel] @ K.T)[:, np.newaxis]:
      sol = cov.data[sel][:, sel] @ K.T
      sol = np.sum(K * sol.T, axis=1, keepdims=True)
      # Reshape back to (n_src, ..., 1)
      sol.shape = stc.data.shape[:-1] + (1,)
      stc = stc.__class__(
          sol, stc.vertices, stc.tmin, stc.tstep, stc.subject
      )
      if combine:  # combine the three directions
          logger.info("    Combining the current components...")
          np.sqrt(stc.data, out=stc.data)
          stc = stc.magnitude()
          stc.data *= stc.data
      logger.info("[done]")
      return stc
  
imports: from copy import deepcopy
  from math import sqrt
  import numpy
  from scipy import linalg
  from scipy.stats import chi2
  from .._fiff.constants import FIFF
  from .._fiff.matrix import _read_named_matrix, _transpose_named_matrix, write_named_matrix
  from .._fiff.open import fiff_open
  from .._fiff.pick import channel_type, pick_channels, pick_info, pick_types
  from .._fiff.proj import _electrode_types, _needs_eeg_average_ref_proj, _read_proj, _write_proj, make_projector
  from .._fiff.tag import find_tag
  from .._fiff.tree import dir_tree_find
  from .._fiff.write import end_block, start_and_end_file, start_block, write_coord_trans, write_float, write_float_matrix, write_int, write_string
  from ..cov import Covariance, _read_cov, _write_cov, compute_whitener, prepare_noise_cov
  from ..epochs import BaseEpochs, EpochsArray
  from ..evoked import Evoked, EvokedArray
  from ..fixes import _safe_svd
  from ..forward import _read_forward_meas_info, _select_orient_forward, compute_depth_prior, compute_orient_prior, convert_forward_solution, is_fixed_orient
  from ..forward.forward import _triage_loose, write_forward_meas_info
  from ..html_templates import _get_html_template
  from ..io import BaseRaw
  from ..source_estimate import _get_src_type, _make_stc
  from ..source_space._source_space import _get_src_nn, _get_vertno, _read_source_spaces_from_tree, _write_source_spaces_to_fid, find_source_space_hemi, label_src_vertno_sel
  from ..surface import _normal_orth
  from ..time_frequency.tfr import _check_tfr_complex
  from ..transforms import _ensure_trans, transform_surface_to
  from ..utils import _check_compensation_grade, _check_depth, _check_fname, _check_option, _check_src_normal, _validate_type, _verbose_safe_false, check_fname, logger, repr_html, verbose, warn
  from ._eloreta import _compute_eloreta
label_desc: Use image node_id 21 for calling a global function with example usage: apply_inverse_cov(cov, info, inverse_operator, nave, lambda2, method, pick_ori, prepared, label, method_params, use_cps, verbose) and returns: stc