code: def backward(self, grad_outputs):
      boxes, box_ind = self.saved_tensors
  
      grad_outputs = grad_outputs.contiguous()
      grad_image = torch.zeros_like(grad_outputs).resize_(*self.im_size)
  
      if grad_outputs.is_cuda:
          _backend.crop_and_resize_gpu_backward(
              grad_outputs, boxes, box_ind, grad_image
          )
      else:
          _backend.crop_and_resize_backward(
              grad_outputs, boxes, box_ind, grad_image
          )
  
      return grad_image, None, None
  
file_path: medicaldetectiontoolkit/cuda_functions/roi_align_3D/roi_align/crop_and_resize.py
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Function
  from ._ext import crop_and_resize
label_desc: Use image node_id 3 for calling the CropAndResizeFunction obj's underlying member method code with example usage: obj.backward(grad_outputs) and returns: grad_image,  None,  None