code: def predict(self, data, alpha=0.01, max_iter=2000, **kwargs):
          
          edge_model = GraphLasso(alpha=alpha, max_iter=max_iter)
          edge_model.fit(data.values)
  
          return nx.relabel_nodes(nx.DiGraph(edge_model.get_precision()),
                                  {idx: i for idx, i in enumerate(data.columns)})
docs: Predict the graph skeleton.
  
          Args:
              data (pandas.DataFrame): observational data
              alpha (float): regularization parameter
              max_iter (int): maximum number of iterations
  
          Returns:
              networkx.Graph: Graph skeleton