code: def test_ipex_channels_last_inference(self):
      model = DummyMultiInputModel()
      x1 = torch.rand(10, 256, 256)  # 3-dim input test
      x2 = torch.rand(10, 3, 256, 256)  # 4-dim input test
      x3 = x2.tolist()  # input without .to() method
  
      ipex_channels_last_model = InferenceOptimizer.trace(
          model, accelerator=None, channels_last=True, use_ipex=True
      )
      with InferenceOptimizer.get_context(ipex_channels_last_model):
          ipex_channels_last_model(x1, x2, x3)
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(
              ipex_channels_last_model, tmp_dir_name
          )
          load_model = InferenceOptimizer.load(tmp_dir_name, model)
          load_model(x1, x2, x3)
  
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 4 for calling the IPEXJITInference obj's underlying member method code with example usage: obj.test_ipex_channels_last_inference() without return types