class_docstr: Computes attribution using guided backpropagation. Guided backpropagation
  computes the gradient of the target output with respect to the input,
  but gradients of ReLU functions are overridden so that only
  non-negative gradients are backpropagated.
  
  More details regarding the guided backpropagation algorithm can be found
  in the original paper here:
  https://arxiv.org/abs/1412.6806
  
  Warning: Ensure that all ReLU operations in the forward function of the
  given model are performed using a module (nn.module.ReLU).
  If nn.functional.ReLU is used, gradients are not overridden appropriately.
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docstr: Args:
  
      model (nn.Module):  The reference to PyTorch model instance.
docstr_tok: ["Args",":","model","(","nn.Module",")",":","The","reference","to","PyTorch","model","instance","."]
code: def __init__(self, model: Module) -> None:
      r"""
      Args:
  
          model (nn.Module):  The reference to PyTorch model instance.
      """
      ModifiedReluGradientAttribution.__init__(
          self, model, use_relu_grad_output=False
      )
  
imports: import warnings
  from typing import Any, List, Tuple, Union
  import torch
  import torch.nn.functional
  from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple, _register_backward_hook
  from captum._utils.gradient import apply_gradient_requirements, undo_gradient_requirements
  from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.attribution import GradientAttribution
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
  from torch.utils.hooks import RemovableHandle
label_desc: Use image node_id 1 to create a new GuidedBackprop object from inherited base classes: ModifiedReluGradientAttribution with example: obj = GuidedBackprop(model)