code: def test_ipex_jit_channels_last_3d_inference(self):
      model = DummyModelWith3d()
      x1 = torch.rand(32, 3, 3, 224, 224)  # 5-dim input test
      x2 = 3
      ipex_jit_channels_last_model = InferenceOptimizer.trace(
          model, accelerator="jit", use_ipex=True, channels_last=True
      )
      with InferenceOptimizer.get_context(ipex_jit_channels_last_model):
          ipex_jit_channels_last_model(x1, x2)
      assert ipex_jit_channels_last_model.channels_last_available == [
          "channels_last_3d",
          "original",
      ]
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(
              ipex_jit_channels_last_model, tmp_dir_name
          )
          load_model = InferenceOptimizer.load(tmp_dir_name, model)
          load_model(x1, x2)
  
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 9 for calling the IPEXJITInference obj's underlying member method code with example usage: obj.test_ipex_jit_channels_last_3d_inference() without return types