code: def _get_layer_path(model):
      if model.__class__.__name__ == "VGG":
          return ["features", "30"]  # pool5
      elif model.__class__.__name__ == "GoogleNet":
          return ["pool5"]
      elif model.__class__.__name__ == "ResNet":
          return ["avgpool"]  # layer4
      elif model.__class__.__name__ == "Inception3":
          return ["Mixed_7c", "branch_pool"]  # ['conv2d_94'], 'mixed10'
      else:  # TODO: guess layer for other networks?
          return None
  
file_path: tensorwatch/tensorwatch/saliency/saliency.py
imports: from .gradcam import GradCAMExplainer
  from .backprop import VanillaGradExplainer, GradxInputExplainer, SaliencyExplainer, IntegrateGradExplainer, DeconvExplainer, GuidedBackpropExplainer, SmoothGradExplainer
  from .deeplift import DeepLIFTRescaleExplainer
  from .occlusion import OcclusionExplainer
  from .epsilon_lrp import EpsilonLrp
  from .lime_image_explainer import LimeImageExplainer, LimeImagenetExplainer
  import skimage.transform
  import torch
  import math
  from ..None import image_utils
label_desc: Use image node_id 2 for calling a global function with example usage: _get_layer_path(model) and returns: list, list, list, list, None