code: def main(args):
      # load and preprocess dataset
      data = load_data(args)
      g = data[0]
      if args.gpu < 0:
          cuda = False
      else:
          cuda = True
          g = g.to(args.gpu)
      features = g.ndata["feat"]
      labels = g.ndata["label"]
      train_mask = g.ndata["train_mask"]
      val_mask = g.ndata["val_mask"]
      test_mask = g.ndata["test_mask"]
      in_feats = features.shape[1]
      n_classes = data.num_classes
      n_edges = g.num_edges()
      print(
          """----Data statistics------'
        #Edges %d
        #Classes %d
        #Train samples %d
        #Val samples %d
        #Test samples %d"""
          % (
              n_edges,
              n_classes,
              train_mask.sum().item(),
              val_mask.sum().item(),
              test_mask.sum().item(),
          )
      )
  
      # graph preprocess and calculate normalization factor
      g = g.remove_self_loop().add_self_loop()
      n_edges = g.num_edges()
      us, vs = g.edges(order="eid")
      udeg, vdeg = 1 / torch.sqrt(
          g.in_degrees(us).float()
      ), 1 / torch.sqrt(g.in_degrees(vs).float())
      pseudo = torch.cat([udeg.unsqueeze(1), vdeg.unsqueeze(1)], dim=1)
  
      # create GraphSAGE model
      model = MoNet(
          g,
          in_feats,
          args.n_hidden,
          n_classes,
          args.n_layers,
          args.pseudo_dim,
          args.n_kernels,
          args.dropout,
      )
  
      if cuda:
          model.cuda()
      loss_fcn = torch.nn.CrossEntropyLoss()
  
      # use optimizer
      optimizer = torch.optim.Adam(
          model.parameters(), lr=args.lr, weight_decay=args.weight_decay
      )
  
      # initialize graph
      mean = 0
      for epoch in range(args.n_epochs):
          model.train()
          if epoch >= 3:
              t0 = time.time()
          # forward
          logits = model(features, pseudo)
          loss = loss_fcn(logits[train_mask], labels[train_mask])
  
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          if epoch >= 3:
              mean = (mean * (epoch - 3) + (time.time() - t0)) / (
                  epoch - 2
              )
              acc = evaluate(model, features, pseudo, labels, val_mask)
              print(
                  "Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
                  "ETputs(KTEPS) {:.2f}".format(
                      epoch,
                      mean,
                      loss.item(),
                      acc,
                      n_edges / mean / 1000,
                  )
              )
  
      print()
      acc = evaluate(model, features, pseudo, labels, test_mask)
      print("Test Accuracy {:.4f}".format(acc))
  
file_path: dgl/examples/pytorch/monet/citation.py
imports: import argparse
  import time
  import networkx
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl import DGLGraph
  from dgl.data import load_data, register_data_args
  from dgl.nn.pytorch.conv import GMMConv
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types