code: def train(device, A, ndata, dataset, model):
      # Create sampler & dataloader.
      train_idx = dataset.train_idx.to(device)
      val_idx = dataset.val_idx.to(device)
  
      train_dataloader = torch.utils.data.DataLoader(
          train_idx, batch_size=1024, shuffle=True
      )
      val_dataloader = torch.utils.data.DataLoader(
          val_idx, batch_size=1024
      )
  
      optimizer = torch.optim.Adam(
          model.parameters(), lr=1e-3, weight_decay=5e-4
      )
  
      fanouts = [4000, 4000, 4000]
      for epoch in range(20):
          model.train()
          total_loss = 0
          for it, seeds in enumerate(train_dataloader):
              sampled_matrices, x, y = multilayer_sample(
                  A, fanouts, seeds, ndata
              )
              y_hat = model(sampled_matrices, x)
              loss = F.cross_entropy(y_hat, y)
              optimizer.zero_grad()
              loss.backward()
              optimizer.step()
              total_loss += loss.item()
  
          acc = evaluate(
              model, A, val_dataloader, ndata, dataset.num_classes
          )
          print(
              "Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} ".format(
                  epoch, total_loss / (it + 1), acc.item()
              )
          )
  
imports: import argparse
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  from dgl.data import AsNodePredDataset
  from dgl.sparse import sp_broadcast_v
  from ogb.nodeproppred import DglNodePropPredDataset
label_desc: Use image node_id 4 for calling a global function with example usage: train(device, A, ndata, dataset, model) without return types