code: def to_CAG(self):
          
  
          G = nx.DiGraph()
          for (name, attrs) in self.nodes(data=True):
              if attrs["type"] == "variable":
                  for pred_fn in self.predecessors(name):
                      if not any(
                          fn_type in pred_fn
                          for fn_type in ("condition", "decision")
                      ):
                          for pred_var in self.predecessors(pred_fn):
                              G.add_node(
                                  self.nodes[pred_var]["basename"],
                                  **self.nodes[pred_var],
                              )
                              G.add_node(attrs["basename"], **attrs)
                              G.add_edge(
                                  self.nodes[pred_var]["basename"],
                                  attrs["basename"],
                              )
                  if attrs["is_loop_index"]:
                      G.add_edge(attrs["basename"], attrs["basename"])
  
          return G
docs: Export to a Causal Analysis Graph (CAG) PyGraphviz AGraph object.
          The CAG shows the influence relationships between the variables and
          elides the function nodes.