code: def prepare_data(device, args):
      data = load_dataset(args.dataset)
      g, n_classes, train_nid, val_nid, test_nid = data
      g = g.to(device)
      in_feats = g.ndata["feat"].shape[1]
      feats = preprocess(g, g.ndata["feat"], args)
      labels = g.ndata["label"]
      # move to device
      train_nid = train_nid.to(device)
      val_nid = val_nid.to(device)
      test_nid = test_nid.to(device)
      train_feats = [x[train_nid] for x in feats]
      train_labels = labels[train_nid]
      return (
          feats,
          labels,
          train_feats,
          train_labels,
          in_feats,
          n_classes,
          train_nid,
          val_nid,
          test_nid,
      )
  
imports: import argparse
  import os
  import time
  import dgl
  import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
  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,  train_feats,  train_labels,  in_feats,  n_classes,  train_nid,  val_nid,  test_nid