code: def _override_backward(self):
      class _ReLU(Function):
          @staticmethod
          def forward(ctx, input):
              output = torch.clamp(input, min=0)
              ctx.save_for_backward(output)
              return output
  
          @staticmethod
          def backward(ctx, grad_output):
              (output,) = ctx.saved_tensors
              mask1 = (output > 0).float()
              mask2 = (grad_output > 0).float()
              grad_inp = mask1 * mask2 * grad_output
              grad_output.copy_(grad_inp)
              return grad_output
  
      def new_forward(self, x):
          return _ReLU.apply(x)
  
      def replace(m):
          if m.__class__.__name__ == "ReLU":
              m.forward = types.MethodType(new_forward, m)
  
      self.model.apply(replace)
  
imports: import numpy
  from torch.autograd import Variable, Function
  import torch
  import types
label_desc: Use image node_id 2 for calling the GuidedBackpropExplainer obj's underlying member method code with example usage: obj._override_backward() and returns: _ReLU, output, grad_output