code: def generate_attention(
      model: nn.Module,
      image_tensor: torch.Tensor,
      method="transformer_attribution",
      class_index: int = None,
      img_size: int = 224,
      device: str = None,
  ) -> np.ndarray:
      """
      Generate attention visualization of the model.
      https://openaccess.thecvf.com/content/CVPR2021/papers/
      Chefer_Transformer_Interpretability_Beyond_Attention_Visualization_CVPR_2021_paper.pdf
      Args:
          model (nn.Module):
              Model to visualize.
          image_tensor (torch.Tensor):
              Image tensor.
          method (str):
              `full`, `rollout` or `transformer_attribution`. Default is `transformer_attribution`.
          class_index (int):
              Class index.
          img_size (int):
              Image size.
          device (str):
              Model device, cpu or cuda
  
      Returns:
          (`numpy.ndarray`):
              Attention map.
      """
      if device is None:
          device = "cuda" if torch.cuda.is_available() else "cpu"
      model.to(device)
      model.eval()
      attribution_generator = LRP(model)
      image_relevance = attribution_generator.generate_lrp(
          image_tensor.unsqueeze(0).to(device=device),
          method=method,
          index=class_index,
      ).detach()
      vis = _reshape_attr_and_get_heatmap(
          image_relevance, image_tensor, img_size=img_size
      )
      return vis
  
file_path: towhee/towhee/models/visualization/transformer_visualization.py
imports: from typing import Any
  from torch import nn
  import torch
  from torchvision import transforms
  import numpy
  from towhee.utils.pil_utils import PILImage
  from towhee.utils.cv2_utils import cv2
  from towhee.trainer.utils.file_utils import is_matplotlib_available
  from towhee.utils.log import models_log
label_desc: Use image node_id 3 for calling a global function with example usage: generate_attention(model, image_tensor, method, class_index, img_size, device) and returns: vis