code: def mean_covariance(
      X=None,
      metric="riemann",
      sample_weight=None,
      covmats=None,
      **kwargs,
  ):
      """Mean of matrices according to a metric.
  
      Compute the mean of a set of matrices according to a metric [1]_.
  
      Parameters
      ----------
      X : ndarray, shape (n_matrices, n, n)
          Set of matrices.
      metric : string, default='riemann'
          The metric for mean, can be: 'ale', 'alm', 'euclid', 'harmonic',
          'identity', 'kullback_sym', 'logdet', 'logeuclid', 'riemann',
          'wasserstein', or a callable function.
      sample_weight : None | ndarray, shape (n_matrices,), default=None
          Weights for each matrix. If None, it uses equal weights.
      **kwargs : dict
          The keyword arguments passed to the sub function.
  
      Returns
      -------
      M : ndarray, shape (n, n)
          Mean of matrices.
  
      References
      ----------
      .. [1] `Review of Riemannian distances and divergences, applied to
          SSVEP-based BCI
          <https://hal.archives-ouvertes.fr/LISV/hal-03015762v1>`_
          S. Chevallier, E. K. Kalunga, Q. Barthélemy, E. Monacelli.
          Neuroinformatics, Springer, 2021, 19 (1), pp.93-106
      """
      X = _deprecate_covmats(covmats, X)
      mean_function = _check_mean_function(metric)
      M = mean_function(
          X,
          sample_weight=sample_weight,
          **kwargs,
      )
      return M
  
imports: from copy import deepcopy
  import numpy
  import warnings
  from .ajd import ajd_pham
  from .base import sqrtm, invsqrtm, logm, expm, powm
  from .distance import distance_riemann
  from .geodesic import geodesic_riemann
  from .utils import check_weights
label_desc: Use image node_id 14 for calling a global function with example usage: mean_covariance(X, metric, sample_weight, covmats) and returns: M