class_docstr: Compute gradient-based saliency maps based on both ``GuidedBackpropGrad`` and ``SmoothGrad``.
class_docstr_tok: ["Compute","gradient-based","saliency","maps","based","on","both","``","GuidedBackpropGrad","``","and","``","SmoothGrad","``","."]
code: def __call__(
      self,
      x: torch.Tensor,
      index: torch.Tensor | int | None = None,
      **kwargs: Any,
  ) -> torch.Tensor:
      with replace_modules_temp(
          self.model, "relu", _GradReLU(), strict_match=False
      ):
          return super().__call__(x, index, **kwargs)
  
imports: from __future__ import annotations
  from functools import partial
  from typing import Any, Callable
  import torch
  from monai.networks.utils import replace_modules_temp
  from monai.utils.module import optional_import
  from monai.visualize.class_activation_maps import ModelWithHooks
label_desc: Use image node_id 1 for calling the GuidedBackpropSmoothGrad obj's underlying member method code with example usage: obj.__call__(x, index) and returns: super