code: def run_train(
      rank, data, train_data, val_data, test_data, world_size
  ):
      if rank == 0:
          print("Setting up Data Loaders...")
      train_edge_label_idx = train_data[
          ("user", "to", "item")
      ].edge_label_index
      train_edge_label_idx = train_edge_label_idx.split(
          train_edge_label_idx.size(1) // world_size, dim=1
      )[rank].clone()
      train_loader = LinkNeighborLoader(
          data=train_data,
          num_neighbors=[8, 4],
          edge_label_index=(
              ("user", "to", "item"),
              train_edge_label_idx,
          ),
          neg_sampling="binary",
          batch_size=2048,
          shuffle=True,
          num_workers=16,
          drop_last=True,
      )
  
      val_loader = LinkNeighborLoader(
          data=val_data,
          num_neighbors=[8, 4],
          edge_label_index=(
              ("user", "to", "item"),
              val_data[("user", "to", "item")].edge_label_index,
          ),
          edge_label=val_data[("user", "to", "item")].edge_label,
          batch_size=2048,
          shuffle=False,
          num_workers=16,
      )
  
      test_loader = LinkNeighborLoader(
          data=test_data,
          num_neighbors=[8, 4],
          edge_label_index=(
              ("user", "to", "item"),
              test_data[("user", "to", "item")].edge_label_index,
          ),
          edge_label=test_data[("user", "to", "item")].edge_label,
          batch_size=2048,
          shuffle=False,
          num_workers=16,
      )
  
      def train():
          model.train()
  
          total_loss = total_examples = 0
          for batch in tqdm.tqdm(train_loader):
              batch = batch.to(rank)
              optimizer.zero_grad()
  
              pred = model(
                  batch.x_dict,
                  batch.edge_index_dict,
                  batch["user", "item"].edge_label_index,
              )
              loss = F.binary_cross_entropy_with_logits(
                  pred, batch["user", "item"].edge_label
              )
  
              loss.backward()
              optimizer.step()
              total_loss += float(loss)
              total_examples += pred.numel()
  
          return total_loss / total_examples
  
      @torch.no_grad()
      def test(loader):
          model.eval()
  
          preds, targets = [], []
          for batch in tqdm.tqdm(loader):
              batch = batch.to(rank)
  
              pred = (
                  model(
                      batch.x_dict,
                      batch.edge_index_dict,
                      batch["user", "item"].edge_label_index,
                  )
                  .sigmoid()
                  .view(-1)
                  .cpu()
              )
              target = batch["user", "item"].edge_label.long().cpu()
  
              preds.append(pred)
              targets.append(target)
  
          pred = torch.cat(preds, dim=0).numpy()
          target = torch.cat(targets, dim=0).numpy()
  
          return roc_auc_score(target, pred)
  
      os.environ["MASTER_ADDR"] = "localhost"
      os.environ["MASTER_PORT"] = "12355"
      dist.init_process_group("nccl", rank=rank, world_size=world_size)
      model = Model(
          num_users=data["user"].num_nodes,
          num_items=data["item"].num_nodes,
          hidden_channels=64,
          out_channels=64,
      ).to(rank)
      # Initialize lazy modules
      for batch in train_loader:
          batch = batch.to(rank)
          _ = model(
              batch.x_dict,
              batch.edge_index_dict,
              batch["user", "item"].edge_label_index,
          )
          break
      model = DistributedDataParallel(model, device_ids=[rank])
      optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
      for epoch in range(1, 21):
          loss = train()
          if rank == 0:
              val_auc = test(val_loader)
              test_auc = test(test_loader)
          if rank == 0:
              print(
                  f"Epoch: {epoch:02d}, Loss: {loss:4f}, Val: {val_auc:.4f}, "
                  f"Test: {test_auc:.4f}"
              )
  
imports: import os
  import os.path
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn.functional
  import tqdm
  from sklearn.metrics import roc_auc_score
  from torch.nn import Embedding, Linear
  from torch.nn.parallel import DistributedDataParallel
  import torch_geometric.transforms
  from torch_geometric.datasets import Taobao
  from torch_geometric.loader import LinkNeighborLoader
  from torch_geometric.nn import SAGEConv
  from torch_geometric.utils.convert import to_scipy_sparse_matrix
label_desc: Use image node_id 1 for calling a global function with example usage: run_train(rank, data, train_data, val_data, test_data, world_size) and returns: unknown, roc_auc_score