code: def get_image_saliency_results(
      model,
      raw_image,
      input,
      label,
      device=None,
      methods=[
          "lime_imagenet",
          "gradcam",
          "smooth_grad",
          "guided_backprop",
          "deeplift",
          "grad_x_input",
      ],
      layer_path=None,
  ):
      results = []
      for method in methods:
          sal = get_saliency(
              model,
              raw_image,
              input,
              label,
              device=device,
              method=method,
          )
  
          if sal is not None:
              results.append(
                  ImageSaliencyResult(raw_image, sal, method)
              )
      return results
  
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 4 for calling a global function with example usage: get_image_saliency_results(model, raw_image, input, label, device, methods, layer_path) and returns: results