code: def test_pipeline_inference(self):
      model = resnet18(num_classes=10)
      pipeline = Pipeline(
          [
              ("preprocess", preprocess, {}),
              ("inference", model, {}),
          ]
      )
      inputs = list(range(10))
      outputs = pipeline.run(inputs)
      assert len(outputs) == 10 and all(
          map(lambda o: o.shape == (1, 10), outputs)
      )
  
file_path: BigDL/python/nano/test/pytorch/tests/inference/test_pipeline.py
imports: import platform
  from unittest import TestCase
  import pytest
  import torch
  from bigdl.nano.pytorch import Pipeline
  from torchvision.models import resnet18
label_desc: Use image node_id 2 for calling the TestPipeline obj's underlying member method code with example usage: obj.test_pipeline_inference() without return types