code: def train(device, A, features, dataset, num_classes, model):
      # Create sampler & dataloader.
      train_set = dataset.tasks[0].train_set
      train_dataloader = create_dataloader(
          A, [10, 10, 10], train_set, features, device
      )
  
      valid_set = dataset.tasks[0].validation_set
      val_dataloader = create_dataloader(
          A, [10, 10, 10], valid_set, features, device
      )
  
      optimizer = torch.optim.Adam(
          model.parameters(), lr=1e-3, weight_decay=5e-4
      )
  
      for epoch in range(10):
          model.train()
          total_loss = 0
          for it, data in enumerate(train_dataloader):
              node_feature = data.node_features["feat"].float()
              blocks = data.sampled_subgraphs
              y = data.labels
              y_hat = model(blocks, node_feature)
              loss = F.cross_entropy(y_hat, y)
              optimizer.zero_grad()
              loss.backward()
              optimizer.step()
              total_loss += loss.item()
  
          acc = evaluate(model, val_dataloader, num_classes)
          print(
              "Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} ".format(
                  epoch, total_loss / (it + 1), acc.item()
              )
          )
  
imports: import argparse
  from functools import partial
  import dgl.graphbolt
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  from dgl.graphbolt.subgraph_sampler import SubgraphSampler
  from torch.utils.data import functional_datapipe
label_desc: Use image node_id 4 for calling a global function with example usage: train(device, A, features, dataset, num_classes, model) without return types