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, use_relu_grad_output: bool = False
  ) -> None:
      r"""
      Args:
  
          model (nn.Module): The reference to PyTorch model instance.
      """
      GradientAttribution.__init__(self, model)
      self.model = model
      self.backward_hooks: List[RemovableHandle] = []
      self.use_relu_grad_output = use_relu_grad_output
      assert isinstance(self.model, torch.nn.Module), (
          "Given model must be an instance of torch.nn.Module to properly hook"
          " ReLU layers."
      )
  
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 ModifiedReluGradientAttribution object from inherited base classes: GradientAttribution with example: obj = ModifiedReluGradientAttribution(model, use_relu_grad_output)