code: def benchmark(mnm, nm, fxn):
      tms = []
      for _ in range(3):
          st = time.perf_counter_ns()
          ret = fxn()
          tms.append(time.perf_counter_ns() - st)
      print(f"{mnm:15s} {nm:25s} {min(tms)*1e-6:7.2f} ms")
      CSV[nm] = min(tms) * 1e-6
      return min(tms), ret
  
file_path: tinygrad/test/external/external_model_benchmark.py
imports: import csv, pathlib, time, numpy
  from os import getenv
  import torch
  import onnx
  from onnx.helper import tensor_dtype_to_np_dtype
  import onnxruntime
  from onnx2torch import convert
  from extra.onnx import get_run_onnx
  from tinygrad.helpers import OSX, DEBUG, fetch
  from tinygrad import Tensor, Device
label_desc: Use image node_id 1 for calling a global function with example usage: benchmark(mnm, nm, fxn) and returns: min,  ret