content: from torch import nn
  from torch.autograd import Function
  from torch.autograd.function import once_differentiable
  from torch.nn.modules.utils import _pair
  
  # import alphaction._custom_cuda_ext as _C
  
  
  class _ROIPool3d(Function):
      @staticmethod
      def forward(ctx, input, roi, output_size, spatial_scale):
          ctx.output_size = _pair(output_size)
          ctx.spatial_scale = spatial_scale
          ctx.input_shape = input.size()
          output, argmax = _C.roi_pool_3d_forward(
              input, roi, spatial_scale, output_size[0], output_size[1]
          )
          ctx.save_for_backward(input, roi, argmax)
          return output
  
      @staticmethod
      @once_differentiable
      def backward(ctx, grad_output):
          input, rois, argmax = ctx.saved_tensors
          output_size = ctx.output_size
          spatial_scale = ctx.spatial_scale
          bs, ch, l, h, w = ctx.input_shape
          grad_input = _C.roi_pool_3d_backward(
              grad_output,
              input,
              rois,
              argmax,
              spatial_scale,
              output_size[0],
              output_size[1],
              bs,
              ch,
              l,
              h,
              w,
          )
          return grad_input, None, None, None
  
  
  roi_pool_3d = _ROIPool3d.apply
  
  
  class ROIPool3d(nn.Module):
      def __init__(self, output_size, spatial_scale):
          super(ROIPool3d, self).__init__()
          self.output_size = output_size
          self.spatial_scale = spatial_scale
  
      def forward(self, input, rois):
          return roi_pool_3d(input, rois, self.output_size, self.spatial_scale)
  
      def __repr__(self):
          tmpstr = self.__class__.__name__ + "("
          tmpstr += "output_size=" + str(self.output_size)
          tmpstr += ", spatial_scale=" + str(self.spatial_scale)
          tmpstr += ")"
          return tmpstr