code: def train(
      proc_id,
      nprocs,
      device,
      g,
      train_idx,
      val_idx,
      test_idx,
      model,
      use_uva,
  ):
      # Create PyTorch DataLoader for constructing blocks
      n_edges = g.num_edges()
      train_seeds = torch.arange(n_edges).to(device)
      labels = g.ndata["label"].to("cpu")
  
      sampler = NeighborSampler([10, 25], prefetch_node_feats=["feat"])
      sampler = as_edge_prediction_sampler(
          sampler,
          exclude="reverse_id",
          # For each edge with ID e in Reddit dataset, the reverse edge is e ± |E|/2.
          reverse_eids=torch.cat(
              [
                  torch.arange(n_edges // 2, n_edges),
                  torch.arange(0, n_edges // 2),
              ]
          ).to(train_seeds),
          # num_negs = 1, neg_share = False
          negative_sampler=NegativeSampler(
              g, 1, False, device if use_uva else None
          ),
      )
      train_dataloader = DataLoader(
          g,
          train_seeds,
          sampler,
          device=device,
          batch_size=10000,
          shuffle=True,
          drop_last=False,
          num_workers=0,
          use_ddp=True,
          use_uva=use_uva,
      )
      opt = torch.optim.Adam(model.parameters(), lr=0.003)
      loss_fcn = CrossEntropyLoss()
      iter_pos = []
      iter_neg = []
      for epoch in range(10):
          tic = time.time()
          model.train()
          for step, (
              input_nodes,
              pos_graph,
              neg_graph,
              blocks,
          ) in enumerate(train_dataloader):
              x = blocks[0].srcdata["feat"]
              y_hat = model(blocks, x)
              loss = loss_fcn(y_hat, pos_graph, neg_graph)
              opt.zero_grad()
              loss.backward()
              opt.step()
  
              if step % 20 == 0 and proc_id == 0:  # log every 20 steps
                  # gpu memory reserved by PyTorch
                  gpu_mem_alloc = (
                      torch.cuda.max_memory_allocated() / 1000000
                      if torch.cuda.is_available()
                      else 0
                  )
                  print(
                      f"Epoch {epoch:05d} | Step {step:05d} | Loss {loss.item():.4f} | GPU {gpu_mem_alloc:.1f} MB"
                  )
  
          t = time.time() - tic
          if proc_id == 0:
              print(f"Epoch Time(s): {t:.4f}")
          if (epoch + 1) % 5 == 0:  # eval every 5 epochs
              pred = evaluate(
                  proc_id, model, g, device, use_uva
              )  # in parallel
              if proc_id == 0:
                  # only master proc does the accuracy computation
                  eval_acc, test_acc = compute_acc_unsupervised(
                      pred, labels, train_idx, val_idx, test_idx
                  )
                  print(
                      f"Epoch {epoch:05d} | Eval F1-score {eval_acc:.4f} | Test F1-Score {test_acc:.4f}"
                  )
  
imports: import argparse
  import os
  import time
  import dgl.function
  import dgl.nn
  import numpy
  import sklearn.linear_model
  import sklearn.metrics
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data import AsNodePredDataset, RedditDataset
  from dgl.dataloading import as_edge_prediction_sampler, DataLoader, MultiLayerFullNeighborSampler, NeighborSampler
  from dgl.multiprocessing import shared_tensor
  from ogb.nodeproppred import DglNodePropPredDataset
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 3 for calling a global function with example usage: train(proc_id, nprocs, device, g, train_idx, val_idx, test_idx, model, use_uva) without return types