code: def test_bf16_jit_channels_last_various_input_sample(self):
      model = DummyMultiInputModel()
      x1 = torch.rand(1, 8, 8)  # 3-dim input test
      x2 = torch.rand(1, 3, 8, 8)  # 4-dim input test
      x3 = [1, 2, 3, 4]  # input without .to() method
      bf16_jit_channels_last_model = InferenceOptimizer.quantize(
          model, precision="bf16", channels_last=True, accelerator="jit"
      )
      with InferenceOptimizer.get_context(bf16_jit_channels_last_model):
          bf16_jit_channels_last_model(x1, x2, x3)
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(
              bf16_jit_channels_last_model, tmp_dir_name
          )
          load_model = InferenceOptimizer.load(tmp_dir_name, model)
          load_model(x1, x2, x3)
  
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 10 for calling the Pytorch1_11 obj's underlying member method code with example usage: obj.test_bf16_jit_channels_last_various_input_sample() without return types