code: def test_dali_preprocess(monkeypatch, file, expected_result):
      monkeypatch.syspath_prepend(EXAMPLE_ROOT_DIR_RESNET)
  
      serialized_file = os.path.join(REPO_ROOT_DIR, MODEL_PTH_FILE)
      if not os.path.exists(serialized_file):
          response = requests.get(
              "https://download.pytorch.org/models/resnet18-f37072fd.pth",
              allow_redirects=True,
          )
          assert response.status_code == 200
          with open(serialized_file, "wb") as f:
              f.write(response.content)
  
      dali_params = {
          "dali": {
              "batch_size": 1,
              "num_threads": 1,
              "device_id": 0,
              "seed": 12,
          },
      }
  
      handler = DALIImageClassifier()
      ctx = MockContext(
          model_pt_file=REPO_ROOT_DIR.joinpath(
              MODEL_PTH_FILE
          ).as_posix(),
          model_dir=EXAMPLE_ROOT_DIR_DALI,
          model_file="model.py",
      )
      ctx.model_yaml_config = dali_params
  
      handler.initialize(ctx)
      data = {}
  
      with open(
          Path(CURR_FILE_PATH) / "test_data" / file, "rb"
      ) as image:
          image_file = image.read()
          byte_array_type = bytearray(image_file)
          data["body"] = byte_array_type
  
      x = handler.preprocess([data])
      x = handler.inference(x)
      x = handler.postprocess(x)
      labels = list(x[0].keys())
  
      assert labels == expected_result
  
imports: import os
  from pathlib import Path
  import pytest
  import requests
  from ts.torch_handler.dali_image_classifier import DALIImageClassifier
  from ts.torch_handler.unit_tests.test_utils.mock_context import MockContext
label_desc: Use image node_id 1 for calling a global function with example usage: test_dali_preprocess(monkeypatch, file, expected_result) without return types