code: def score(self, X_test, y=None):
          
          if isinstance(self.precision_, list):
              print("Warning: returning a list of scores.")
  
          S_test, lam_scale_test = _init_coefs(X_test, method=self.init_method)
          error = self.cov_error(S_test, score_metric=self.score_metric)
  
          # maximize score with -error
          return -error
docs: Computes the score between cov/prec of sample covariance of X_test
          and X via 'score_metric'.
  
          Note: We want to maximize score so we return the negative error.
  
          Parameters
          ----------
          X_test : array-like, shape = [n_samples, n_features]
              Test data of which we compute the likelihood, where n_samples is
              the number of samples and n_features is the number of features.
              X_test is assumed to be drawn from the same distribution than
              the data used in fit (including centering).
  
          y : not used.
  
          Returns
          -------
          result : float or list of floats
              The negative of the min error between `self.covariance_` and
              the sample covariance of X_test.