code: def _objective(mle, precision_, alpha):
      """Evaluation of the graphical-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 operator
  import sys
  import time
  import warnings
  from numbers import Integral, Real
  import numpy
  from scipy import linalg
  from ..base import _fit_context
  from ..exceptions import ConvergenceWarning
  from ..linear_model import _cd_fast
  from ..linear_model import lars_path_gram
  from ..model_selection import check_cv, cross_val_score
  from ..utils._param_validation import Interval, StrOptions, validate_params
  from ..utils.parallel import Parallel, delayed
  from ..utils.validation import _is_arraylike_not_scalar, check_random_state, check_scalar
  from .None import EmpiricalCovariance, empirical_covariance, log_likelihood
label_desc: Use image node_id 1 for calling a global function with example usage: _objective(mle, precision_, alpha) and returns: cost