code: def benchmark_torch_function(iters, f, *args, **kwargs):
      f(*args, **kwargs)
      f(*args, **kwargs)
      torch.cuda.synchronize()
      start_event = torch.cuda.Event(enable_timing=True)
      end_event = torch.cuda.Event(enable_timing=True)
      start_event.record()
      for _ in range(iters):
          f(*args, **kwargs)
      end_event.record()
      torch.cuda.synchronize()
      # elapsed_time has a resolution of 0.5 microseconds:
      # but returns milliseconds, so we need to multiply it to increase resolution
      return start_event.elapsed_time(end_event) * 1000 / iters, *f(
          *args, **kwargs
      )
  
file_path: pytorch/benchmarks/transformer/better_transformer_vs_mha_functional.py
imports: import argparse
  import itertools
  import json
  import random
  import warnings
  from collections import defaultdict, OrderedDict
  from pathlib import Path
  from pprint import pprint
  from typing import Optional
  import numpy
  import torch
  from prettytable import PrettyTable
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: benchmark_torch_function(iters, f) and returns: unknown,  unknown