docstr: Calculate the gradient of the cross-entropy loss w.r.t the image.
  Args:
      data(numpy.ndarray): input data with shape (size, height, width,
      channels).
      label(int): Label used to calculate the gradient.
  Return:
      numpy.ndarray: gradient of the cross-entropy loss w.r.t the image
          with the shape (height, width, channel).
docstr_tok: ["Calculate","the","gradient","of","the","cross-entropy","loss","w.r.t","the","image",".","Args",":","data","(","numpy.ndarray",")",":","input","data","with","shape","(","size",",","height",",","width",",","channels",")",".","label","(","int",")",":","Label","used","to","calculate","the","gradient",".","Return",":","numpy.ndarray",":","gradient","of","the","cross-entropy","loss","w.r.t","the","image","with","the","shape","(","height",",","width",",","channel",")","."]
code: def gradient(self, data, label):
      """
      Calculate the gradient of the cross-entropy loss w.r.t the image.
      Args:
          data(numpy.ndarray): input data with shape (size, height, width,
          channels).
          label(int): Label used to calculate the gradient.
      Return:
          numpy.ndarray: gradient of the cross-entropy loss w.r.t the image
              with the shape (height, width, channel).
      """
  
      import torch
  
      scaled_data = self._process_input(data)
  
      # logging.info(scaled_data)
  
      scaled_data = torch.from_numpy(scaled_data).to(self._device)
      scaled_data.requires_grad = True
  
      label = np.array([label])
      label = torch.from_numpy(label).to(self._device)
      # label = torch.Tensor(label).to(self._device)
  
      output = self.predict_tensor(scaled_data).to(self._device)
  
      # loss=self._loss(output, label)
      ce = nn.CrossEntropyLoss()
      loss = -ce(output, label)
  
      # 计算梯度
      # Zero all existing gradients
      self._model.zero_grad()
      loss.backward()
  
      # 技巧 梯度也是tensor 需要转换成np
      grad = scaled_data.grad.cpu().numpy().copy()
  
      return grad.reshape(scaled_data.shape)
  
file_path: AdvBox/adversarialbox/models/pytorch.py
imports: from __future__ import absolute_import
  from __future__ import print_function
  import numpy
  import os
  from .base import Model
  import logging
  import torchvision
  from torch.autograd import Variable
  import torch.nn
label_desc: Use image node_id 5 for calling the PytorchModel obj's underlying member method code with example usage: obj.gradient(data, label) and returns: grad