class_docstr: Given an input image ``x``, calling this class will perform the forward pass, then set to zero
  all activations except one (defined by ``index``) and propagate back to the image to achieve a gradient-based
  saliency map.
  
  If ``index`` is None, argmax of the output logits will be used.
  
  See also:
  
      - Simonyan et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
        (https://arxiv.org/abs/1312.6034)
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code: def __call__(
      self,
      x: torch.Tensor,
      index: torch.Tensor | int | None = None,
      **kwargs: Any,
  ) -> torch.Tensor:
      return self.get_grad(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 5 for calling the VanillaGrad obj's underlying member method code with example usage: obj.__call__(x, index) and returns: self