code: def test_ipex_jit_inference_additional_attrs(self):
      model = ResNet18(
          10, pretrained=False, include_top=False, freeze=True
      )
      #  patch a attr
      model.channels = 3
  
      def hello():
          print("hello world!")
  
      # patch a function
      model.hello = hello
  
      # test jit + ipex
      new_model = InferenceOptimizer.trace(
          model,
          accelerator="jit",
          use_ipex=True,
          input_sample=self.data_sample,
      )
      with InferenceOptimizer.get_context(new_model):
          new_model(self.data_sample)
      assert new_model.channels == 3
      new_model.hello()
      with pytest.raises(
          AttributeError,
          match="'PytorchIPEXJITModel' object has no attribute 'strange_call'",
      ):
          new_model.strange_call()
  
      # save & load with original model
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(new_model, tmp_dir_name)
          load_model = InferenceOptimizer.load(
              tmp_dir_name, model=model
          )
      assert load_model.channels == 3
      load_model.hello()
      with pytest.raises(
          AttributeError,
          match="'PytorchIPEXJITModel' object has no attribute 'strange_call'",
      ):
          load_model.strange_call()
  
      # test jit + ipex + inplace
      new_model = InferenceOptimizer.trace(
          model,
          accelerator="jit",
          use_ipex=True,
          inplace=True,
          input_sample=self.data_sample,
      )
      with InferenceOptimizer.get_context(new_model):
          new_model(self.data_sample)
      assert new_model.channels == 3
      new_model.hello()
  
      model = ResNet18(
          10, pretrained=False, include_top=False, freeze=True
      )
      #  patch a attr
      model.channels = 3
  
      def hello():
          print("hello world!")
  
      # patch a function
      model.hello = hello
  
      # test jit
      new_model = InferenceOptimizer.trace(
          model, accelerator="jit", input_sample=self.data_sample
      )
      with InferenceOptimizer.get_context(new_model):
          new_model(self.data_sample)
      assert new_model.channels == 3
      new_model.hello()
  
      # save & load with original model
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(new_model, tmp_dir_name)
          load_model = InferenceOptimizer.load(
              tmp_dir_name, model=model
          )
      assert load_model.channels == 3
      load_model.hello()
  
      with pytest.raises(
          AttributeError,
          match="'PytorchIPEXJITModel' object has no attribute 'strange_call'",
      ):
          load_model.strange_call()
  
      # test ipex
      new_model = InferenceOptimizer.trace(model, use_ipex=True)
      with InferenceOptimizer.get_context(new_model):
          new_model(self.data_sample)
      assert new_model.channels == 3
      new_model.hello()
      with pytest.raises(AttributeError):
          new_model.width
  
      # save & load with original model
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(new_model, tmp_dir_name)
          load_model = InferenceOptimizer.load(
              tmp_dir_name, model=model
          )
      assert load_model.channels == 3
      load_model.hello()
  
      with pytest.raises(
          AttributeError,
          match="'PytorchIPEXJITModel' object has no attribute 'strange_call'",
      ):
          load_model.strange_call()
  
      # test ipex inplace
      new_model = InferenceOptimizer.trace(
          model, use_ipex=True, inplace=True
      )
      with InferenceOptimizer.get_context(new_model):
          new_model(self.data_sample)
      assert new_model.channels == 3
      new_model.hello()
      with pytest.raises(AttributeError):
          new_model.width
  
      model = ResNet18(
          10, pretrained=False, include_top=False, freeze=True
      )
      #  patch a attr
      model.channels = 3
  
      def hello():
          print("hello world!")
  
      # patch a function
      model.hello = hello
  
      # test channels_last
      new_model = InferenceOptimizer.trace(model, channels_last=True)
      with InferenceOptimizer.get_context(new_model):
          new_model(self.data_sample)
      assert new_model.channels == 3
      new_model.hello()
      with pytest.raises(AttributeError):
          new_model.width
  
imports: import os
  from unittest import TestCase
  import operator
  import pytest
  import torch
  from torch.utils.data import TensorDataset, DataLoader, Dataset
  from test.pytorch.utils._train_torch_lightning import create_data_loader, data_transform
  from torch import nn
  import operator
  from bigdl.nano.pytorch import InferenceOptimizer
  from bigdl.nano.pytorch.vision.models import vision
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_10, TORCH_VERSION_LESS_2_0
  from bigdl.nano.utils.common import compare_version, _avx512_checker, _avx2_checker
  import tempfile
  from typing import List
  import numpy
label_desc: Use image node_id 10 for calling the IPEXJITInference obj's underlying member method code with example usage: obj.test_ipex_jit_inference_additional_attrs() without return types