code: def test_ipex_jit_inference_jit_method(self):
      class Net(nn.Module):
          def __init__(self):
              super().__init__()
  
          def forward(self, x):
              return torch.arange(len(x))
  
      model = Net()
  
      input_sample = torch.rand(1, 3, 1, 1)
      input = torch.rand(5, 3, 1, 1)
      expected_output_len = 5
  
      # test with jit.script (with ipex)
      accmodel = InferenceOptimizer.trace(
          model,
          accelerator="jit",
          use_ipex=True,
          input_sample=input_sample,
          jit_method="script",
      )
      with InferenceOptimizer.get_context(accmodel):
          output = accmodel(input)
      assert output.shape[0] == expected_output_len
  
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(accmodel, tmp_dir_name)
          loaded_model = InferenceOptimizer.load(tmp_dir_name)
      with InferenceOptimizer.get_context(loaded_model):
          output = loaded_model(input)
      assert output.shape[0] == expected_output_len
      assert loaded_model.jit_method == "script"
  
      # test with jit.trace (with ipex)
      accmodel = InferenceOptimizer.trace(
          model,
          accelerator="jit",
          use_ipex=True,
          input_sample=input_sample,
          jit_method="trace",
      )
      with InferenceOptimizer.get_context(accmodel):
          output = accmodel(input)
      assert output.shape[0] != expected_output_len
  
      # test with deafult jit_method
      accmodel = InferenceOptimizer.trace(
          model, accelerator="jit", input_sample=input_sample
      )
      with InferenceOptimizer.get_context(accmodel):
          output = accmodel(input)
      assert output != output.shape[0] != expected_output_len
  
      # test with invalidInputError
      with pytest.raises(RuntimeError):
          InferenceOptimizer.trace(
              model,
              accelerator="jit",
              input_sample=input_sample,
              jit_method="scriptttt",
          )
  
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 14 for calling the IPEXJITInference obj's underlying member method code with example usage: obj.test_ipex_jit_inference_jit_method() and returns: torch