code: def benchmark_concat(
      num_inputs, input_dim, axis, add_axis, iterations
  ):
      input_names = [f"input{i}" for i in range(num_inputs)]
      for n in input_names:
          workspace.FeedBlob(
              n, np.random.randn(*input_dim).astype(np.float32)
          )
  
      net = core.Net("benchmark_net")
      net.Concat(
          input_names,
          ["output", "split_info"],
          axis=axis,
          add_axis=add_axis,
      )
      workspace.CreateNet(net)
  
      runtimes = workspace.BenchmarkNet(net.Name(), 1, iterations, True)
      print(
          f"{num_inputs * np.prod(input_dim) * 4 / runtimes[1] / 1e6} GB/s"
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: benchmark_concat(num_inputs, input_dim, axis, add_axis, iterations) without return types