code: def test_jit_int8(self):
      model = ResNet18(
          10, pretrained=False, include_top=False, freeze=True
      )
      loader = create_data_loader(
          data_dir, batch_size, num_workers, data_transform
      )
      jit_int8_model = InferenceOptimizer.quantize(
          model,
          calib_dataloader=loader,
          accelerator="jit",
          precision="int8",
      )
      input_sample = next(iter(loader))[0]
      with InferenceOptimizer.get_context(jit_int8_model):
          output1 = jit_int8_model(input_sample)
      with InferenceOptimizer.get_context(model):
          output2 = model(input_sample)
      np.testing.assert_allclose(output1, output2, atol=2e-1)
  
      with tempfile.TemporaryDirectory() as tmp_dir:
          InferenceOptimizer.save(jit_int8_model, tmp_dir)
          loaded_model = InferenceOptimizer.load(tmp_dir)
      with InferenceOptimizer.get_context(loaded_model):
          output3 = loaded_model(input_sample)
      np.testing.assert_allclose(output1, output3, atol=2e-1)
  
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 18 for calling the IPEXJITInference obj's underlying member method code with example usage: obj.test_jit_int8() without return types