code: def _objective(mle, precision_, alpha):
      """Evaluation of the graph-lasso objective function
  
      the objective function is made of a shifted scaled version of the
      normalized log-likelihood (i.e. its empirical mean over the samples) and a
      penalisation term to promote sparsity
      """
      p = precision_.shape[0]
      cost = -2.0 * log_likelihood(mle, precision_) + p * np.log(
          2 * np.pi
      )
      cost += alpha * (
          np.abs(precision_).sum() - np.abs(np.diag(precision_)).sum()
      )
      return cost
  
imports: import warnings
  import operator
  import sys
  import time
  import numpy
  from scipy import linalg
  from .empirical_covariance_ import empirical_covariance, EmpiricalCovariance, log_likelihood
  from ..exceptions import ConvergenceWarning
  from ..utils.extmath import pinvh
  from ..utils.validation import check_random_state, check_array
  from ..linear_model import lars_path
  from ..linear_model import cd_fast
  from ..model_selection import check_cv, cross_val_score
  from ..externals.joblib import Parallel, delayed
  import collections
label_desc: Use image node_id 1 for calling a global function with example usage: _objective(mle, precision_, alpha) and returns: cost