code: def main():
      global NUM_REPEATS
      global NUM_REPEAT_OF_REPEATS
  
      parser = argparse.ArgumentParser(
          description="Run the __torch_function__ benchmarks."
      )
      parser.add_argument(
          "--nreps",
          "-n",
          type=int,
          default=NUM_REPEATS,
          help="The number of repeats for one measurement.",
      )
      parser.add_argument(
          "--nrepreps",
          "-m",
          type=int,
          default=NUM_REPEAT_OF_REPEATS,
          help="The number of measurements.",
      )
      args = parser.parse_args()
  
      NUM_REPEATS = args.nreps
      NUM_REPEAT_OF_REPEATS = args.nrepreps
  
      types = (
          torch.tensor,
          SubTensor,
          WithTorchFunction,
          SubWithTorchFunction,
      )
  
      for t in types:
          tensor_1 = t([1.0])
          tensor_2 = t([2.0])
  
          bench_min, bench_std = bench(tensor_1, tensor_2)
          print(
              f"Type {t.__name__} had a minimum time of {10**6 * bench_min} us"
              f" and a standard deviation of {(10**6) * bench_std} us."
          )
  
imports: import argparse
  import time
  import torch
  from common import SubTensor, SubWithTorchFunction, WithTorchFunction
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types