code: def _backprop(self, inp, ind):
      inp.requires_grad = True
      if inp.grad is not None:
          inp.grad.zero_()
      if ind.grad is not None:
          ind.grad.zero_()
      self.model.eval()
      self.model.zero_grad()
  
      output = self.model(inp)
      if ind is None:
          ind = output.max(1)[1]
      grad_out = output.clone()
      grad_out.fill_(0.0)
      grad_out.scatter_(1, ind.unsqueeze(0).t(), 1.0)
      output.backward(grad_out)
      return inp.grad
  
imports: import numpy
  from torch.autograd import Variable, Function
  import torch
  import types
label_desc: Use image node_id 2 for calling the VanillaGradExplainer obj's underlying member method code with example usage: obj._backprop(inp, ind) and returns: inp