code: def _backward_hook(
      self,
      module: Module,
      grad_input: Union[Tensor, Tuple[Tensor, ...]],
      grad_output: Union[Tensor, Tuple[Tensor, ...]],
  ):
      to_override_grads = (
          grad_output if self.use_relu_grad_output else grad_input
      )
      if isinstance(to_override_grads, tuple):
          return tuple(
              F.relu(to_override_grad)
              for to_override_grad in to_override_grads
          )
      else:
          return F.relu(to_override_grads)
  
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 4 for calling the ModifiedReluGradientAttribution obj's underlying member method code with example usage: obj._backward_hook(module, grad_input, grad_output) and returns: tuple, F