class_docstr: This tests whether the entrypoints run without errors without quantitative measure of the outputs.
docstr: Test torch_inference.py
code: def test_torch_inference(
      self,
      get_functions,
      get_config,
      project_path,
      get_dummy_inference_image,
      transforms_config,
  ):
      """Test torch_inference.py"""
      get_parser, infer = get_functions
      model = get_model(get_config("padim"))
      export(
          task=TaskType.SEGMENTATION,
          transform=transforms_config,
          input_size=(100, 100),
          model=model,
          export_mode=ExportMode.TORCH,
          export_root=project_path,
      )
      arguments = get_parser().parse_args(
          [
              "--weights",
              project_path + "/weights/torch/model.pt",
              "--input",
              get_dummy_inference_image,
              "--output",
              project_path + "/output.png",
          ]
      )
      infer(arguments)
  
file_path: anomalib/tests/pre_merge/tools/test_torch_entrypoint.py
imports: from __future__ import annotations
  import sys
  from importlib.util import find_spec
  import pytest
  from anomalib.data import TaskType
  from anomalib.deploy import ExportMode, export
  from anomalib.models import get_model
label_desc: Use image node_id 2 for calling the TestTorchInferenceEntrypoint obj's underlying member method code with example usage: obj.test_torch_inference(get_functions, get_config, project_path, get_dummy_inference_image, transforms_config) without return types