code: def test_jit_jump_input(self):
      model = JumpInputNet()
      x1 = torch.randn(1, 10)
      x3 = torch.randn(1, 10)
      target = model(x1, None, x3)
      # test jit
      with pytest.raises(RuntimeError):
          opt_model = InferenceOptimizer.trace(
              model,
              accelerator="jit",
              input_sample=(x1, None, x3),
              jit_method="trace",
          )
  
      opt_model = InferenceOptimizer.trace(
          model,
          accelerator="jit",
          input_sample=None,
          example_kwarg_inputs={"x1": x1, "x3": x3},
          jit_method="trace",
      )
      output1 = opt_model(x1, x3)
      # TODO: accept opt_model(x1, None, x3)
      np.testing.assert_allclose(
          output1.detach().numpy(), target.detach().numpy(), atol=1e-5
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
          InferenceOptimizer.save(opt_model, tmp_dir)
          loaded_model = InferenceOptimizer.load(tmp_dir)
      output2 = loaded_model(x1, x3)
      np.testing.assert_allclose(
          output2.detach().numpy(), target.detach().numpy(), atol=1e-5
      )
  
      # test jit ipex
      opt_model = InferenceOptimizer.trace(
          model,
          accelerator="jit",
          use_ipex=True,
          input_sample=None,
          example_kwarg_inputs={"x1": x1, "x3": x3},
          jit_method="trace",
      )
      output1 = opt_model(x1, x3)
      np.testing.assert_allclose(
          output1.detach().numpy(), target.detach().numpy(), atol=1e-5
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
          InferenceOptimizer.save(opt_model, tmp_dir)
          loaded_model = InferenceOptimizer.load(tmp_dir)
      output2 = loaded_model(x1, x3)
      np.testing.assert_allclose(
          output2.detach().numpy(), output1.detach().numpy(), atol=1e-5
      )
  
      # test jit int8
      # custom dataloader
      class CustomDataset(Dataset):
          def __init__(self):
              self.x1 = x1
              self.x3 = x3
  
          def __len__(self):
              return 1
  
          def __getitem__(self, idx):
              return (self.x1[0], None, self.x3[0]), target
  
      def fn(input_data):
          return input_data[0]
  
      dataset = CustomDataset()
      loader = DataLoader(dataset, batch_size=1, collate_fn=fn)
  
      opt_model = InferenceOptimizer.quantize(
          model,
          calib_data=loader,
          input_sample=None,
          example_kwarg_inputs={"x1": x1, "x3": x3},
          accelerator="jit",
          precision="int8",
          jit_method="trace",
      )
      output1 = opt_model(x1, x3)
      np.testing.assert_allclose(
          output1.detach().numpy(), target.detach().numpy(), atol=1e-2
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
          InferenceOptimizer.save(opt_model, tmp_dir)
          loaded_model = InferenceOptimizer.load(tmp_dir)
      output2 = loaded_model(x1, x3)
      np.testing.assert_allclose(
          output2.detach().numpy(), output1.detach().numpy(), atol=1e-5
      )
  
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 19 for calling the IPEXJITInference obj's underlying member method code with example usage: obj.test_jit_jump_input() and returns: input_data, int, self,  None,  self,  target