code: def track_time(data):
      dataset = utils.process_data(data)
      device = utils.get_bench_device()
  
      user_ntype = dataset.user_ntype
      item_ntype = dataset.item_ntype
      textset = dataset.textset
  
      batch_size = 32
      random_walk_length = 2
      random_walk_restart_prob = 0.5
      num_random_walks = 10
      num_neighbors = 3
      num_layers = 2
      num_workers = 0
      hidden_dims = 16
      lr = 3e-5
      iter_start = 3
      iter_count = 10
  
      g = dataset[0]
      # Sampler
      batch_sampler = ItemToItemBatchSampler(
          g, user_ntype, item_ntype, batch_size
      )
      neighbor_sampler = NeighborSampler(
          g,
          user_ntype,
          item_ntype,
          random_walk_length,
          random_walk_restart_prob,
          num_random_walks,
          num_neighbors,
          num_layers,
      )
      collator = PinSAGECollator(
          neighbor_sampler, g, item_ntype, textset
      )
      dataloader = DataLoader(
          batch_sampler,
          collate_fn=collator.collate_train,
          num_workers=num_workers,
      )
      dataloader_test = DataLoader(
          torch.arange(g.num_nodes(item_ntype)),
          batch_size=batch_size,
          collate_fn=collator.collate_test,
          num_workers=num_workers,
      )
  
      # Model
      model = PinSAGEModel(
          g, item_ntype, textset, hidden_dims, num_layers
      ).to(device)
      # Optimizer
      opt = torch.optim.Adam(model.parameters(), lr=lr)
  
      model.train()
  
      print("start training...")
      # For each batch of head-tail-negative triplets...
      for batch_id, (pos_graph, neg_graph, blocks) in enumerate(
          dataloader
      ):
          # Copy to GPU
          for i in range(len(blocks)):
              blocks[i] = blocks[i].to(device)
          pos_graph = pos_graph.to(device)
          neg_graph = neg_graph.to(device)
  
          loss = model(pos_graph, neg_graph, blocks).mean()
          opt.zero_grad()
          loss.backward()
          opt.step()
  
          # start timer at before iter_start
          if batch_id == iter_start - 1:
              t0 = time.time()
          elif (
              batch_id == iter_count + iter_start - 1
          ):  # time iter_count iterations
              break
  
      t1 = time.time()
  
      return (t1 - t0) / iter_count
  
imports: import argparse
  import pickle
  import time
  import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader, IterableDataset
  from ..None import utils
label_desc: Use image node_id 6 for calling a global function with example usage: track_time(data) and returns: unknown