code: def interpret_image_classification(
      model: nn.Module,
      image: Any,
      eval_transform: transforms.Compose,
      method: str,
      fig_size: Tuple = (10, 10),
      cmap: Any = "OrRd",
      pred_label_idx: int = None,
      titles: List = None,
      **kwargs: Any,
  ):
      """
      Use Captum to interpret the specified class of network output.
      Captum should be installed.
      Args:
          model (`nn.Module`):
              Pytorch module.
          image (`Any`):
              The image before do transform.
              It can be produced by either pytorch `DataLoader` or read by `Image.open()` using PIL.
          eval_transform (`transforms.Compose`):
              Evaluation transform.
          method (`method`):
              It can be in ['Occlusion', 'IntegratedGradients', 'GradientShap', 'Saliency'].
          fig_size (`Tuple`):
              Figure plotting size.
          cmap (`Any`):
              Matplotlib colormap.
          pred_label_idx (`int`):
              If None, use the predicted class automatically.
          titles (`List`):
              Plotted titles of the two axs in the figure.
          **kwargs (`Any`):
              Keyword Args.
      Returns:
          (`tuple`)
              Prediction score and label idx. If input label is specified, the prediction score is None.
      """
      if not is_captum_available():
          trainer_log.warning(
              "You should install Captum first. Please run `pip install captum`."
          )
          return None
      from captum.attr import (
          visualization as viz,
      )  # pylint: disable=import-outside-toplevel
  
      assert method in [
          "Occlusion",
          "IntegratedGradients",
          "GradientShap",
          "Saliency",
      ]
  
      def _viz_image_attr_multiple(
          attribution,
          trans_image: torch.Tensor,
          fig_size,
          cmap,
          titles: List[str] = None,
          **kwargs,
      ):
          _ = viz.visualize_image_attr_multiple(
              np.transpose(
                  attribution.squeeze().cpu().detach().numpy(),
                  (1, 2, 0),
              ),
              np.transpose(
                  trans_image.squeeze().cpu().detach().numpy(),
                  (1, 2, 0),
              ),
              ["original_image", "blended_heat_map"],
              ["all", "positive"],
              show_colorbar=True,
              outlier_perc=2,
              fig_size=fig_size,
              cmap=cmap,
              titles=titles,
              **kwargs,
          )
  
      model.eval()
  
      #  prepare image and input
      input_ = eval_transform(image)
      trans_without_norm = copy.deepcopy(eval_transform)
      for trans in trans_without_norm.transforms:
          if trans.__class__.__name__ == "Normalize":
              trans_without_norm.transforms.remove(trans)
      trans_image = trans_without_norm(image)
      if len(trans_image.shape) == 3:
          trans_image = trans_image.unsqueeze(0)
      if len(input_.shape) == 3:
          input_ = input_.unsqueeze(0)
  
      prediction_score = None
      if pred_label_idx is None:
          (
              prediction_score,
              pred_label_idx,
          ) = predict_image_classification(model, input_)
  
      if titles is None:
          titles = ["Origin Image", "Method:" + method]
  
      if method == "Occlusion":
          from captum.attr import (
              Occlusion,
          )  # pylint: disable=import-outside-toplevel
  
          occlusion = Occlusion(model)
  
          attributions_occ = occlusion.attribute(
              input_,
              strides=(3, 8, 8),
              target=pred_label_idx,
              sliding_window_shapes=(3, 15, 15),
              baselines=0,
          )
          _viz_image_attr_multiple(
              attributions_occ,
              trans_image,
              fig_size,
              cmap,
              titles=titles,
              **kwargs,
          )
  
      if method == "IntegratedGradients":
          from captum.attr import (
              NoiseTunnel,
          )  # pylint: disable=import-outside-toplevel
          from captum.attr import (
              IntegratedGradients,
          )  # pylint: disable=import-outside-toplevel
  
          integrated_gradients = IntegratedGradients(model)
          noise_tunnel = NoiseTunnel(integrated_gradients)
  
          attributions_ig_nt = noise_tunnel.attribute(
              input_,
              nt_samples=10,
              nt_type="smoothgrad_sq",
              target=pred_label_idx,
          )
          _viz_image_attr_multiple(
              attributions_ig_nt,
              trans_image,
              fig_size,
              cmap,
              titles=titles,
              **kwargs,
          )
  
      if method == "GradientShap":
          from captum.attr import (
              GradientShap,
              NoiseTunnel,
          )  # pylint: disable=import-outside-toplevel
  
          gradient_shap = GradientShap(model)
          rand_img_dist = torch.cat([input_ * 0, input_ * 1])
          attributions_gs = gradient_shap.attribute(
              input_,
              n_samples=50,
              stdevs=0.0001,
              baselines=rand_img_dist,
              target=pred_label_idx,
          )
          _viz_image_attr_multiple(
              attributions_gs,
              trans_image,
              fig_size,
              cmap,
              titles=titles,
              **kwargs,
          )
  
      if method == "Saliency":
          from captum.attr import (
              Saliency,
          )  # pylint: disable=import-outside-toplevel
  
          saliency = Saliency(model)
          grads = saliency.attribute(input_, target=pred_label_idx)
          _viz_image_attr_multiple(
              grads,
              trans_image,
              fig_size,
              cmap,
              titles=titles,
              **kwargs,
          )
  
      return prediction_score, pred_label_idx
  
imports: from typing import Optional, List, Any, Tuple
  from torchvision import transforms
  from torchvision.io import read_image
  from pathlib import Path
  from torch import nn
  from towhee.utils.pil_utils import PILImage
  from towhee.trainer.optimization.optimization import get_scheduler, get_warmup_steps
  from towhee.trainer.utils.trainer_utils import _construct_scheduler_from_config
  from towhee.trainer.training_config import TrainingConfig
  from towhee.trainer.utils.file_utils import is_captum_available, is_matplotlib_available
  from towhee.utils.log import trainer_log
  import torch
  import torch.nn.functional
  import numpy
  import random
  import os
  import copy
label_desc: Use image node_id 10 for calling a global function with example usage: interpret_image_classification(model, image, eval_transform, method, fig_size, cmap, pred_label_idx, titles) and returns: prediction_score,  pred_label_idx, None