code: def test_openpilot_model(self):
      onnx_model = onnx.load(fetch(OPENPILOT_MODEL))
      run_onnx = get_run_onnx(onnx_model)
      print("got run_onnx")
      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: v.astype(np.float32) for k, v in inputs.items()}
  
      st = time.monotonic()
      print("****** run onnx ******")
      tinygrad_out = run_onnx(inputs)["outputs"]
      mt = time.monotonic()
      print("****** realize ******")
      tinygrad_out.realize()
      mt2 = time.monotonic()
      tinygrad_out = tinygrad_out.numpy()
      et = time.monotonic()
      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"
      )
  
      Tensor.no_grad = True
      torch_out = run_onnx_torch(onnx_model, inputs).numpy()
      Tensor.no_grad = False
      print(tinygrad_out, torch_out)
      np.testing.assert_allclose(
          torch_out, tinygrad_out, atol=1e-4, rtol=1e-2
      )
  
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 2 for calling the TestOnnxModel obj's underlying member method code with example usage: obj.test_openpilot_model() without return types