code: def test_ipex_jit_keyword_argument(self):
      net = MultipleInputNet()
      x1 = torch.randn(32, 10)
      x2 = torch.randn(32, 10)
      y = torch.randn(32, 1)
      dataloader = DataLoader(TensorDataset(x1, x2, y), batch_size=1)
  
      model = InferenceOptimizer.trace(
          net, accelerator=None, use_ipex=True, calib_data=dataloader
      )
      with InferenceOptimizer.get_context(model):
          model(x1, x2)
          # test keyword argument
          model(x1, x2=x2)
          model(x1=x1, x2=x2)
  
      model = InferenceOptimizer.trace(
          net,
          accelerator="jit",
          use_ipex=True,
          calib_data=dataloader,
          enable_onednn=False,
      )
      with InferenceOptimizer.get_context(model):
          model(x1, x2)
          # test keyword argument
          model(x1, x2=x2)
          model(x1=x1, x2=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 13 for calling the IPEXJITInference obj's underlying member method code with example usage: obj.test_ipex_jit_keyword_argument() without return types