code: def test_torch_export_aot_compile(custom_working_directory):
      # Get the path to the custom working directory
      model_dir = custom_working_directory
  
      # Construct the path to the Python script to execute
      script_path = os.path.join(
          EXAMPLE_ROOT_DIR, "resnet18_torch_export.py"
      )
  
      # Get the .pt2 file from torch.export
      cmd = "python " + script_path
      try_and_handle(cmd)
  
      # Handler for Image classification
      handler = ImageClassifier()
  
      # Context definition
      ctx = MockContext(
          model_pt_file=MODEL_SO_FILE,
          model_dir=model_dir.as_posix(),
          model_file=None,
          model_yaml_config_file=MODEL_YAML_CFG_FILE,
      )
  
      torch.manual_seed(42 * 42)
      handler.initialize(ctx)
      handler.context = ctx
      handler.mapping = load_label_mapping(MAPPING_DATA)
  
      data = {}
      with open(TEST_DATA, "rb") as image:
          image_file = image.read()
          byte_array_type = bytearray(image_file)
          data["body"] = byte_array_type
  
      result = handler.handle([data], ctx)
  
      labels = list(result[0].keys())
  
      assert labels == EXPECTED_RESULTS
  
file_path: serve/test/pytest/test_torch_export.py
imports: from pathlib import Path
  import torch
  from pkg_resources import packaging
  from ts.torch_handler.image_classifier import ImageClassifier
  from ts.torch_handler.unit_tests.test_utils.mock_context import MockContext
  from ts.utils.util import load_label_mapping
  from ts_scripts.utils import try_and_handle
  import os
  import pytest
label_desc: Use image node_id 2 for calling a global function with example usage: test_torch_export_aot_compile(custom_working_directory) without return types