code: def log_likelihood(emp_cov, precision):
      """Compute the sample mean of the log_likelihood under a covariance model.
  
      computes the empirical expected log-likelihood (accounting for the
      normalization terms and scaling), allowing for universal comparison (beyond
      this software package)
  
      Parameters
      ----------
      emp_cov : 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
      -------
      sample mean of the log-likelihood
      """
      p = precision.shape[0]
      log_likelihood_ = -np.sum(emp_cov * precision) + _logdet(
          precision
      )
      log_likelihood_ -= p * np.log(2 * np.pi)
      log_likelihood_ /= 2.0
      return log_likelihood_
  
imports: import inspect
  import operator
  import os
  import warnings
  from contextlib import contextmanager
  from io import StringIO
  from math import log
  from pprint import pprint
  import numpy
label_desc: Use image node_id 10 for calling a global function with example usage: log_likelihood(emp_cov, precision) and returns: log_likelihood_