code: def test_benchmark_openpilot_model(self):
      onnx_model = onnx.load(fetch(OPENPILOT_MODEL))
      run_onnx = get_run_onnx(onnx_model)
  
      def get_inputs():
          np_inputs = {
              "input_imgs": np.random.randn(*(1, 12, 128, 256)),
              "big_input_imgs": np.random.randn(*(1, 12, 128, 256)),
              "desire": np.zeros((1, 100, 8)),
              "traffic_convention": np.array([[1.0, 0.0]]),
              "nav_features": np.zeros((1, 256)),
              "features_buffer": np.zeros((1, 99, 128)),
          }
          inputs = {
              k: Tensor(v.astype(np.float32), requires_grad=False)
              for k, v in np_inputs.items()
          }
          return inputs
  
      for _ in range(7):
          inputs = get_inputs()
          st = time.monotonic()
          tinygrad_out = run_onnx(inputs)["outputs"]
          mt = time.monotonic()
          tinygrad_out.realize()
          mt2 = time.monotonic()
          tinygrad_out = tinygrad_out.numpy()
          et = time.monotonic()
          if not CI:
              print(
                  f"ran openpilot model in {(et-st)*1000.0:.2f} ms, waited {(mt2-mt)*1000.0:.2f} ms for realize, {(et-mt2)*1000.0:.2f} ms for GPU queue"
              )
  
      if not CI:
          import cProfile
          import pstats
  
          inputs = get_inputs()
          pr = cProfile.Profile(
              timer=time.perf_counter_ns, timeunit=1e-6
          )
          pr.enable()
      tinygrad_out = run_onnx(inputs)["outputs"]
      tinygrad_out.realize()
      tinygrad_out = tinygrad_out.numpy()
      if not CI:
          pr.disable()
          stats = pstats.Stats(pr)
          stats.dump_stats(temp("net.prof"))
          os.system(
              f"flameprof {temp('net.prof')} > {temp('prof.svg')}"
          )
          ps = stats.sort_stats(pstats.SortKey.TIME)
          ps.print_stats(30)
  
file_path: tinygrad/test/models/test_onnx.py
imports: import os
  import time
  import unittest
  import numpy
  import onnx
  from extra.onnx import get_run_onnx
  from tinygrad.tensor import Tensor
  from tinygrad.helpers import CI, fetch, temp
label_desc: Use image node_id 1 for calling the TestOnnxModel obj's underlying member method code with example usage: obj.test_benchmark_openpilot_model() and returns: inputs