code: def run(rank: int, world_size: int, dataset: PygNodePropPredDataset):
      device = f"xpu:{rank}"
  
      split_idx = dataset.get_idx_split()
      split_idx["train"] = (
          split_idx["train"]
          .split(split_idx["train"].size(0) // world_size, dim=0)[rank]
          .clone()
      )
      data = dataset[0].to(device, "x", "y")
  
      kwargs = dict(batch_size=1024, num_workers=0, pin_memory=True)
      train_loader = NeighborLoader(
          data,
          input_nodes=split_idx["train"],
          num_neighbors=[10, 10, 5],
          **kwargs,
      )
  
      if rank == 0:
          subgraph_loader = NeighborLoader(
              copy.copy(data), num_neighbors=[-1], **kwargs
          )
          evaluator = Evaluator(name="ogbn-products")
  
      torch.manual_seed(12345)
      model = GAT(
          dataset.num_features,
          128,
          dataset.num_classes,
          num_layers=3,
          heads=4,
      ).to(device)
      model = DDP(model, device_ids=[device])
      optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
  
      for epoch in range(1, 21):
          epoch_stats = []
          model.train()
          for batch in train_loader:
              batch = batch.to(device)
              loss, stats = train_step(
                  model,
                  optimizer,
                  batch.x,
                  batch.edge_index,
                  batch.y,
                  batch.batch_size,
              )
              epoch_stats.append(stats)
  
          dist.barrier()
  
          if rank == 0:
              print(f"Epoch: {epoch:02d}, Loss: {loss:.4f}")
  
          print(
              f"Epoch: {epoch:02d}, Rank: {rank}, "
              f"Stats: {get_stats_summary(epoch_stats)}"
          )
  
          if rank == 0 and epoch % 5 == 0:  # Evaluation on a single GPU
              model.eval()
              with torch.no_grad():
                  out = model.module.inference(
                      data.x, device, subgraph_loader
                  )
  
              y_true = data.y.to(out.device)
              y_pred = out.argmax(dim=-1, keepdim=True)
  
              train_acc = evaluator.eval(
                  {
                      "y_true": y_true[split_idx["train"]],
                      "y_pred": y_pred[split_idx["train"]],
                  }
              )["acc"]
              val_acc = evaluator.eval(
                  {
                      "y_true": y_true[split_idx["valid"]],
                      "y_pred": y_pred[split_idx["valid"]],
                  }
              )["acc"]
              test_acc = evaluator.eval(
                  {
                      "y_true": y_true[split_idx["test"]],
                      "y_pred": y_pred[split_idx["test"]],
                  }
              )["acc"]
  
              print(
                  f"Train: {train_acc:.4f}, Val: {val_acc:.4f}, "
                  f"Test: {test_acc:.4f}"
              )
  
          dist.barrier()
  
      dist.destroy_process_group()
  
imports: import copy
  import os
  import os.path
  from typing import Any, Tuple, Union
  import intel_extension_for_pytorch
  import oneccl_bindings_for_pytorch
  import torch
  import torch.distributed
  import torch.nn.functional
  from ogb.nodeproppred import Evaluator, PygNodePropPredDataset
  from torch import Tensor
  from torch.nn import Linear
  from torch.nn.parallel import DistributedDataParallel
  from tqdm import tqdm
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import GATConv
  from torch_geometric.profile import get_stats_summary, profileit
label_desc: Use image node_id 2 for calling a global function with example usage: run(rank, world_size, dataset) without return types