code: def quic_graph_lasso_cv(X, metric):
      
      print("QuicGraphicalLassoCV with:")
      print("   metric: {}".format(metric))
      model = QuicGraphicalLassoCV(
          cv=2,  # cant deal w more folds at small size
          n_refinements=6,
          n_jobs=1,
          init_method="cov",
          score_metric=metric,
      )
      model.fit(X)
      print("   len(cv_lams): {}".format(len(model.cv_lams_)))
      print("   lam_scale_: {}".format(model.lam_scale_))
      print("   lam_: {}".format(model.lam_))
      return model.covariance_, model.precision_, model.lam_
docs: Run QuicGraphicalLassoCV on data with metric of choice.
  
      Compare results with GridSearchCV + quic_graph_lasso.  The number of
      lambdas tested should be much lower with similar final lam_ selected.