code: def log_likelihood(covariance, precision):
      """Computes the log-likelihood between the covariance and precision
      estimate.
  
      Parameters
      ----------
      covariance : 2D ndarray (n_features, n_features)
          Maximum Likelihood Estimator of covariance
  
      precision : 2D ndarray (n_features, n_features)
          The precision matrix of the covariance model to be tested
  
      Returns
      -------
      log-likelihood
      """
      assert covariance.shape == precision.shape
      dim, _ = precision.shape
      log_likelihood_ = (
          -np.sum(covariance * precision)
          + fast_logdet(precision)
          - dim * np.log(2 * np.pi)
      )
      log_likelihood_ /= 2.0
      return log_likelihood_
  
imports: import numpy
  from sklearn.utils.extmath import fast_logdet
label_desc: Use image node_id 1 for calling a global function with example usage: log_likelihood(covariance, precision) and returns: log_likelihood_