code: def parse_test_config(
      test_config: Dict,
  ) -> Tuple[
      List[Type[Attribution]],
      Module,
      Dict[str, Any],
      Module,
      bool,
      bool,
  ]:
      algorithms = cast(
          List[Type[Attribution]], test_config["algorithms"]
      )
      model = test_config["model"]
      args = cast(Dict[str, Any], test_config["attribute_args"])
      layer = test_config["layer"] if "layer" in test_config else None
      noise_tunnel = (
          test_config["noise_tunnel"]
          if "noise_tunnel" in test_config
          else False
      )
      baseline_distr = (
          test_config["baseline_distr"]
          if "baseline_distr" in test_config
          else False
      )
      return (
          algorithms,
          model,
          args,
          layer,
          noise_tunnel,
          baseline_distr,
      )
  
file_path: captum/tests/attr/helpers/gen_test_utils.py
imports: import typing
  from typing import Any, cast, Dict, List, Tuple, Type, Union
  from captum.attr._core.lime import Lime
  from captum.attr._models.base import _get_deep_layer_name
  from captum.attr._utils.attribution import Attribution
  from torch.nn import Module
label_desc: Use image node_id 2 for calling a global function with example usage: parse_test_config(test_config) and returns: algorithms,  model,  args,  layer,  noise_tunnel,  baseline_distr