code: def track_time(graph_name, format, fraction, return_uv):
      device = utils.get_bench_device()
      graph = utils.get_graph(graph_name, format)
      coo_graph = utils.get_graph(graph_name, "coo")
      graph = graph.to(device)
      eids = np.random.choice(
          np.arange(graph.num_edges(), dtype=np.int64),
          int(graph.num_edges() * fraction),
      )
      eids = torch.tensor(eids, device="cpu", dtype=torch.int64)
      u, v = coo_graph.find_edges(eids)
      del coo_graph, eids
      u = u.to(device)
      v = v.to(device)
      # dry run
      for i in range(10):
          out = graph.edge_ids(u[0], v[0])
  
      # timing
  
      with utils.Timer() as t:
          for i in range(3):
              edges = graph.edge_ids(u, v, return_uv=return_uv)
  
      return t.elapsed_secs / 3
  
imports: import time
  import dgl
  import numpy
  import torch
  from ..None import utils
label_desc: Use image node_id 1 for calling a global function with example usage: track_time(graph_name, format, fraction, return_uv) and returns: unknown