code: def test_bf16_ipex_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.quantize(
          model,
          precision="bf16",
          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.quantize(
          model,
          precision="bf16",
          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.quantize(
          model,
          precision="bf16",
          accelerator="jit",
          input_sample=input_sample,
      )
      with InferenceOptimizer.get_context(accmodel):
          output = accmodel(input)
      assert output.shape[0] != expected_output_len
  
      # test with invalidInputError
      with pytest.raises(RuntimeError):
          InferenceOptimizer.quantize(
              model,
              precision="bf16",
              accelerator="jit",
              input_sample=input_sample,
              jit_method="scriptttt",
          )
  
imports: from unittest import TestCase
  import pytest
  import torch
  from torch import nn
  from torch.utils.data import TensorDataset, DataLoader
  from bigdl.nano.pytorch import InferenceOptimizer
  from torchvision.models.resnet import resnet18
  from unittest.mock import PropertyMock, patch
  from bigdl.nano.utils.common import _avx512_checker
  import tempfile
  from typing import List
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_2_0
  from bigdl.nano.utils.common import compare_version, _avx512_checker, _avx2_checker
  import operator
  import numpy
label_desc: Use image node_id 7 for calling the Pytorch1_11 obj's underlying member method code with example usage: obj.test_bf16_ipex_jit_method() and returns: torch