code: def _compute_error(
      comp_cov, covariance_, precision_, score_metric="frobenius"
  ):
      """Computes the covariance error vs. comp_cov.
  
      Parameters
      ----------
      comp_cov : array-like, shape = (n_features, n_features)
          The precision to compare with.
          This should normally be the test sample covariance/precision.
  
      scaling : bool
          If True, the squared error norm is divided by n_features.
          If False (default), the squared error norm is not rescaled.
  
      score_metric : str
          The type of norm used to compute the error between the estimated
          self.precision, self.covariance and the reference `comp_cov`.
          Available error types:
  
          - 'frobenius' (default): sqrt(tr(A^t.A))
          - 'spectral': sqrt(max(eigenvalues(A^t.A))
          - 'kl': kl-divergence
          - 'quadratic': quadratic loss
          - 'log_likelihood': negative log likelihood
  
      squared : bool
          Whether to compute the squared error norm or the error norm.
          If True (default), the squared error norm is returned.
          If False, the error norm is returned.
      """
      if score_metric == "frobenius":
          return np.linalg.norm(
              np.triu(comp_cov - covariance_, 1), ord="fro"
          )
      elif score_metric == "spectral":
          error = comp_cov - covariance_
          return np.amax(np.linalg.svdvals(np.dot(error.T, error)))
      elif score_metric == "kl":
          return metrics.kl_loss(comp_cov, precision_)
      elif score_metric == "quadratic":
          return metrics.quadratic_loss(comp_cov, precision_)
      elif score_metric == "log_likelihood":
          return -metrics.log_likelihood(comp_cov, precision_)
      else:
          raise NotImplementedError(
              (
                  "Must be frobenius, spectral, kl, "
                  "quadratic, or log_likelihood"
              )
          )
  
file_path: skggm/inverse_covariance/inverse_covariance.py
imports: from __future__ import absolute_import
  import numpy
  from sklearn.base import BaseEstimator
  from .None import metrics
  from .rank_correlation import spearman_correlation, kendalltau_correlation
label_desc: Use image node_id 2 for calling a global function with example usage: _compute_error(comp_cov, covariance_, precision_, score_metric) and returns: np, np, metrics, metrics, unknown