code: def test_trainer_compile_with_onnx_quantize_additional_attributes(
      self,
  ):
      model = ResNet18(
          10, pretrained=False, include_top=False, freeze=True
      )
      loss = nn.CrossEntropyLoss()
      optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
      trainer = Trainer(max_epochs=1)
  
      pl_model = Trainer.compile(model, loss, optimizer)
      x = torch.rand((10, 256, 256, 3))
      y = torch.ones((10,), dtype=torch.long)
      ds = TensorDataset(x, y)
      train_loader = DataLoader(
          ds, batch_size=2, collate_fn=customized_collate_fn
      )
      trainer.fit(pl_model, train_loader)
      # patch a attribute
      pl_model.channels = 3
  
      def hello():
          print("hello world!")
  
      # patch a function
      pl_model.hello = hello
  
      # normal usage without tunning
      onnx_model = InferenceOptimizer.quantize(
          pl_model,
          accelerator="onnxruntime",
          method="qlinear",
          calib_data=train_loader,
          thread_num=2,
      )
      with InferenceOptimizer.get_context(onnx_model):
          assert torch.get_num_threads() == 2
          x = torch.rand((2, 3, 256, 256))
          output = onnx_model(x)
          assert output.shape == (2, 10)
  
      assert onnx_model.channels == 3
      onnx_model.hello()
      with pytest.raises(
          AttributeError,
          match="'PytorchONNXRuntimeModel' object has no attribute 'width'",
      ):
          onnx_model.width
  
      # save & load without original model
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(onnx_model, tmp_dir_name)
          load_model = InferenceOptimizer.load(tmp_dir_name)
      with pytest.raises(
          AttributeError,
          match="'PytorchONNXRuntimeModel' object has no attribute 'channels'",
      ):
          load_model.channels
      with pytest.raises(AttributeError):
          load_model.hello()
  
      # save & load with original model
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(onnx_model, tmp_dir_name)
          load_model = InferenceOptimizer.load(
              tmp_dir_name, model=pl_model
          )
      assert load_model.channels == 3
      load_model.hello()
      with pytest.raises(
          AttributeError,
          match="'PytorchONNXRuntimeModel' object has no attribute 'width'",
      ):
          onnx_model.width
  
imports: import pytest
  import os
  from unittest import TestCase
  import tempfile
  import torch
  from torch import nn
  from torch.utils.data import TensorDataset, DataLoader, Dataset
  import torchmetrics
  import numpy
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import InferenceOptimizer
  from bigdl.nano.pytorch.vision.models import vision
label_desc: Use image node_id 4 for calling the TestOnnx obj's underlying member method code with example usage: obj.test_trainer_compile_with_onnx_quantize_additional_attributes() without return types