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, SubWithTorchFunc, MetaTensor
  
      for t in types:
          tensor_1 = t(1)
          tensor_2 = t(2)
  
          b_min, b_avg, b_med, b_std = bench(tensor_1, tensor_2)
          print(
              "Type {} time (microseconds):  min: {}, avg: {}, median: {}, and std {}.".format(
                  t.__name__,
                  (10**6 * b_min),
                  (10**6) * b_avg,
                  (10**6) * b_med,
                  (10**6) * b_std,
              )
          )
  
file_path: MONAI/tests/profile_subclass/profiling.py
imports: from __future__ import annotations
  import argparse
  import torch
  from min_classes import SubTensor, SubWithTorchFunc
  from monai.data import MetaTensor
  from monai.utils.profiling import PerfContext
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types