code: def prepare_data(device, args):
      """
      Load dataset and compute neighbor-averaged node features used by SIGN model
      """
      data = load_dataset(args.dataset, device)
      (
          g,
          labels,
          n_classes,
          train_nid,
          val_nid,
          test_nid,
          evaluator,
      ) = data
      in_feats = g.ndata["feat"].shape[1]
      feats = neighbor_average_features(g, args)
      labels = labels.to(device)
      # move to device
      train_nid = train_nid.to(device)
      val_nid = val_nid.to(device)
      test_nid = test_nid.to(device)
      return (
          feats,
          labels,
          in_feats,
          n_classes,
          train_nid,
          val_nid,
          test_nid,
          evaluator,
      )
  
imports: import argparse
  import time
  import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  from dataset import load_dataset
label_desc: Use image node_id 3 for calling a global function with example usage: prepare_data(device, args) and returns: feats,  labels,  in_feats,  n_classes,  train_nid,  val_nid,  test_nid,  evaluator