code: def inductive_split(g):
      """Split the graph into training graph, validation graph, and test graph by training
      and validation masks.  Suitable for inductive models."""
      train_g = g.subgraph(g.ndata["train_mask"])
      val_g = g.subgraph(g.ndata["train_mask"] | g.ndata["val_mask"])
      test_g = g
      return train_g, val_g, test_g
  
imports: import dgl
  import torch
label_desc: Use image node_id 3 for calling a global function with example usage: inductive_split(g) and returns: train_g,  val_g,  test_g