code: def track_time(data):
      dataset = utils.process_data(data)
      device = utils.get_bench_device()
  
      if data == "ogbn-mag":
          n_bases = 2
          l2norm = 0
      else:
          raise ValueError()
  
      fanout = 4
      n_layers = 2
      batch_size = 1024
      n_hidden = 64
      dropout = 0.5
      use_self_loop = True
      lr = 0.01
      iter_start = 3
      iter_count = 10
  
      hg = dataset[0]
      category = dataset.predict_category
      num_classes = dataset.num_classes
      train_mask = hg.nodes[category].data.pop("train_mask")
      train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
      labels = hg.nodes[category].data.pop("labels")
  
      node_feats = {}
      num_nodes = {}
      for ntype in hg.ntypes:
          node_feats[ntype] = (
              hg.nodes[ntype].data["feat"]
              if "feat" in hg.nodes[ntype].data
              else None
          )
          num_nodes[ntype] = hg.num_nodes(ntype)
  
      embed_layer = RelGraphEmbed(
          hg, device, n_hidden, num_nodes, node_feats
      )
      model = EntityClassify(
          hg,
          n_hidden,
          num_classes,
          num_bases=n_bases,
          num_hidden_layers=n_layers - 2,
          dropout=dropout,
          use_self_loop=use_self_loop,
      )
      embed_layer = embed_layer.to(device)
      model = model.to(device)
  
      all_params = itertools.chain(
          model.parameters(), embed_layer.embeds.parameters()
      )
      optimizer = th.optim.Adam(all_params, lr=lr, weight_decay=l2norm)
      sparse_optimizer = th.optim.SparseAdam(
          list(embed_layer.node_embeds.parameters()), lr=lr
      )
  
      sampler = dgl.dataloading.MultiLayerNeighborSampler(
          [fanout] * n_layers
      )
      loader = dgl.dataloading.DataLoader(
          hg,
          {category: train_idx},
          sampler,
          batch_size=batch_size,
          shuffle=True,
          num_workers=4,
      )
  
      print("start training...")
      model.train()
      embed_layer.train()
      optimizer.zero_grad()
      sparse_optimizer.zero_grad()
  
      # Enable dataloader cpu affinitization for cpu devices (no effect on gpu)
      with loader.enable_cpu_affinity():
          for step, (input_nodes, seeds, blocks) in enumerate(loader):
              blocks = [blk.to(device) for blk in blocks]
              seeds = seeds[
                  category
              ]  # we only predict the nodes with type "category"
              batch_tic = time.time()
              emb = embed_layer(blocks[0])
              lbl = labels[seeds].to(device)
              emb = {k: e.to(device) for k, e in emb.items()}
              logits = model(emb, blocks)[category]
              loss = F.cross_entropy(logits, lbl)
              loss.backward()
              optimizer.step()
              sparse_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 itertools
  import time
  import traceback
  import dgl
  import dgl.nn.pytorch
  import torch
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.utils.data import DataLoader
  from ..None import utils
label_desc: Use image node_id 1 for calling a global function with example usage: track_time(data) and returns: unknown