code: def test_ipex_jit_inference_stable_diffusion_unet(self):
      from diffusers.models import UNet2DConditionModel
  
      # reduce model size as action runner has limited memory
      unet = UNet2DConditionModel(
          sample_size=64,
          cross_attention_dim=10,
          attention_head_dim=1,
          down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
          block_out_channels=(32, 64),
          up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"),
          layers_per_block=1,
      )
      latent_shape = (2, 4, 8, 8)
      image_latents = torch.randn(
          latent_shape, device="cpu", dtype=torch.float32
      )
      encoder_hidden_states = torch.randn(
          (2, 6, 10), device="cpu", dtype=torch.float32
      )
      input_sample = (
          image_latents,
          torch.Tensor([980]).long(),
          encoder_hidden_states,
      )
  
      latent_shape2 = (1, 4, 8, 8)  # different shape
      image_latents2 = torch.randn(
          latent_shape2, device="cpu", dtype=torch.float32
      )
      encoder_hidden_states2 = torch.randn(
          (1, 12, 10), device="cpu", dtype=torch.float32
      )
  
      unet(
          image_latents,
          torch.Tensor([980]).long(),
          encoder_hidden_states,
      )
      unet(
          image_latents2,
          torch.Tensor([980]).long(),
          encoder_hidden_states2,
      )
  
      nano_unet = InferenceOptimizer.trace(
          unet,
          accelerator="jit",
          use_ipex=True,
          input_sample=input_sample,
          jit_strict=False,
          weights_prepack=False,
      )
      nano_unet(
          image_latents,
          torch.Tensor([980]).long(),
          encoder_hidden_states,
      )
      nano_unet(
          image_latents2,
          torch.Tensor([980]).long(),
          encoder_hidden_states2,
      )
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(nano_unet, tmp_dir_name)
          new_model = InferenceOptimizer.load(tmp_dir_name)
      new_model(
          image_latents,
          torch.Tensor([980]).long(),
          encoder_hidden_states,
      )
      new_model(
          image_latents2,
          torch.Tensor([980]).long(),
          encoder_hidden_states2,
      )
  
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 17 for calling the IPEXJITInference obj's underlying member method code with example usage: obj.test_ipex_jit_inference_stable_diffusion_unet() without return types