code: def track_time(data, num_negs, batch_size):
      data = utils.process_data(data)
      device = utils.get_bench_device()
      g = data[0]
      g.ndata["features"] = g.ndata["feat"]
      g.ndata["labels"] = g.ndata["label"]
      in_feats = g.ndata["features"].shape[1]
      n_classes = data.num_classes
  
      # Create csr/coo/csc formats before launching training processes with multi-gpu.
      # This avoids creating certain formats in each sub-process, which saves momory and CPU.
      g.create_formats_()
  
      num_epochs = 2
      num_hidden = 16
      num_layers = 2
      fan_out = "10,25"
      lr = 0.003
      dropout = 0.5
      num_workers = 4
      num_negs = 2
      iter_start = 3
      iter_count = 10
  
      n_edges = g.num_edges()
      train_seeds = np.arange(n_edges)
  
      # Create PyTorch DataLoader for constructing blocks
      sampler = dgl.dataloading.MultiLayerNeighborSampler(
          [int(fanout) for fanout in fan_out.split(",")]
      )
      sampler = dgl.dataloading.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=th.cat(
              [
                  th.arange(n_edges // 2, n_edges),
                  th.arange(0, n_edges // 2),
              ]
          ),
          negative_sampler=NegativeSampler(g, num_negs),
      )
      dataloader = dgl.dataloading.DataLoader(
          g,
          train_seeds,
          sampler,
          batch_size=batch_size,
          shuffle=True,
          drop_last=False,
          num_workers=num_workers,
      )
  
      # Define model and optimizer
      model = SAGE(
          in_feats, num_hidden, n_classes, num_layers, F.relu, dropout
      )
      model = model.to(device)
      loss_fcn = CrossEntropyLoss()
      loss_fcn = loss_fcn.to(device)
      optimizer = optim.Adam(model.parameters(), lr=lr)
  
      # Training loop
      avg = 0
      iter_tput = []
      for step, (
          input_nodes,
          pos_graph,
          neg_graph,
          blocks,
      ) in enumerate(dataloader):
          # Load the input features as well as output labels
          batch_inputs = load_subtensor(g, input_nodes, device)
  
          pos_graph = pos_graph.to(device)
          neg_graph = neg_graph.to(device)
          blocks = [block.int().to(device) for block in blocks]
          # Compute loss and prediction
          batch_pred = model(blocks, batch_inputs)
          loss = loss_fcn(batch_pred, pos_graph, neg_graph)
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          # start timer at before iter_start
          if step == iter_start - 1:
              t0 = time.time()
          elif (
              step == iter_count + iter_start - 1
          ):  # time iter_count iterations
              break
  
      t1 = time.time()
  
      return (t1 - t0) / iter_count
  
imports: import time
  import dgl
  import dgl.function
  import dgl.nn.pytorch
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from ..None import utils
label_desc: Use image node_id 3 for calling a global function with example usage: track_time(data, num_negs, batch_size) and returns: unknown