class_docstr: Based on Springenberg and Dosovitskiy et al. https://arxiv.org/abs/1412.6806,
  compute gradient-based saliency maps by backpropagating positive gradients and inputs (see ``_AutoGradReLU``).
  
  See also:
  
      - Springenberg and Dosovitskiy et al. Striving for Simplicity: The All Convolutional Net
        (https://arxiv.org/abs/1412.6806)
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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 GuidedBackpropGrad obj's underlying member method code with example usage: obj.__call__(x, index) and returns: super