code: def graph_lasso(X, num_folds):
      """Estimate inverse covariance via scikit-learn GraphicalLassoCV class."""
      print("GraphLasso (sklearn)")
      model = GraphicalLassoCV(cv=num_folds)
      model.fit(X)
      print("   lam_: {}".format(model.alpha_))
      return model.covariance_, model.precision_, model.alpha_
  
imports: import sys
  import numpy
  import tabulate
  import time
  from sklearn.model_selection import GridSearchCV
  from sklearn.datasets import make_sparse_spd_matrix
  from sklearn.covariance import GraphicalLassoCV, ledoit_wolf
  import matplotlib.pyplot
  from inverse_covariance import QuicGraphicalLasso, QuicGraphicalLassoCV, QuicGraphicalLassoEBIC, AdaptiveGraphicalLasso, ModelAverage
label_desc: Use image node_id 13 for calling a global function with example usage: graph_lasso(X, num_folds) and returns: model,  model,  model