code: def test_onnx_quantize_kwargs(self):
      model = MultiInputModel()
      x1 = torch.randn(100, 28 * 28)
      x2 = torch.randn(100, 28 * 28)
      target = model(x1, x2)
  
      class CustomDataset(Dataset):
          def __init__(self):
              self.x1 = x1
              self.x2 = x2
  
          def __len__(self):
              return 100
  
          def __getitem__(self, idx):
              return (self.x1[idx], self.x2[idx]), target
  
      dataset = CustomDataset()
      loader = DataLoader(dataset, batch_size=1)
  
      with torch.no_grad():
          onnx_model = InferenceOptimizer.quantize(
              model,
              accelerator="onnxruntime",
              precision="int8",
              input_sample=(x1, x2),
              calib_data=loader,
          )
      with InferenceOptimizer.get_context(onnx_model):
          output1 = onnx_model(x1, x2)
          np.testing.assert_almost_equal(
              target.numpy(), output1.numpy(), decimal=0
          )
          output2 = onnx_model(x1, x2=x2)
          np.testing.assert_almost_equal(
              output1.numpy(), output2.numpy(), decimal=5
          )
          output3 = onnx_model(x1=x1, x2=x2)
          np.testing.assert_almost_equal(
              output1.numpy(), output3.numpy(), decimal=5
          )
  
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          InferenceOptimizer.save(onnx_model, tmp_dir_name)
          load_model = InferenceOptimizer.load(tmp_dir_name)
  
      with InferenceOptimizer.get_context(load_model):
          output4 = load_model(x1=x1, x2=x2)
          np.testing.assert_almost_equal(
              output4.numpy(), output4.numpy(), decimal=5
          )
  
imports: import pytest
  import os
  from unittest import TestCase
  import tempfile
  import torch
  from torch import nn
  from torch.utils.data import TensorDataset, DataLoader, Dataset
  import torchmetrics
  import numpy
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import InferenceOptimizer
  from bigdl.nano.pytorch.vision.models import vision
label_desc: Use image node_id 8 for calling the TestOnnx obj's underlying member method code with example usage: obj.test_onnx_quantize_kwargs() and returns: int, self,  self,  target