code: def test_runners(
      graphs: List[str],
      runners: List[Tuple[str, Callable]],
      graph_set: Optional[List[int]],
  ):
      for i, ir in enumerate(graphs):
          _, inputs = load_graph_and_inputs(ir)
          if graph_set and i not in graph_set:
              continue
  
          print(f"Running Graph {i}")
          prev_result = None
          prev_runner_name = None
          for runner in runners:
              runner_name, runner_fn = runner
              try:
                  result = runner_fn(ir, inputs)
                  if prev_result:
                      improvement = (prev_result / result - 1) * 100
                      print(
                          f"{runner_name} : {result:.6f} ms improvement over {prev_runner_name}: improvement: {improvement:.2f}%"
                      )
                  else:
                      print(f"{runner_name} : {result:.6f} ms")
                  prev_result = result
                  prev_runner_name = runner_name
              except RuntimeError:
                  print(
                      f"  Graph {i} failed for {runner_name} :",
                      traceback.format_exc(),
                  )
  
imports: import argparse
  import functools
  import traceback
  from typing import Callable, List, Optional, Tuple
  from torch.utils.jit.log_extract import extract_ir, load_graph_and_inputs, run_baseline_no_fusion, run_nnc, run_nvfuser
label_desc: Use image node_id 1 for calling a global function with example usage: test_runners(graphs, runners, graph_set) without return types