code: def backward(ctx, grad_output):
      if not grad_output.is_cuda:
          raise NotImplementedError(
              "Deformable Conv is not supported on CPUs!"
          )
      input, offset, mask, weight, bias = ctx.saved_tensors
      grad_input = torch.zeros_like(input)
      grad_offset = torch.zeros_like(offset)
      grad_mask = torch.zeros_like(mask)
      grad_weight = torch.zeros_like(weight)
      grad_bias = torch.zeros_like(bias)
      _C.modulated_deform_conv_backward(
          input,
          weight,
          bias,
          ctx._bufs[0],
          offset,
          mask,
          ctx._bufs[1],
          grad_input,
          grad_weight,
          grad_bias,
          grad_offset,
          grad_mask,
          grad_output,
          weight.shape[2],
          weight.shape[3],
          ctx.stride,
          ctx.stride,
          ctx.padding,
          ctx.padding,
          ctx.dilation,
          ctx.dilation,
          ctx.groups,
          ctx.deformable_groups,
          ctx.with_bias,
      )
      if not ctx.with_bias:
          grad_bias = None
  
      return (
          grad_input,
          grad_offset,
          grad_mask,
          grad_weight,
          grad_bias,
          None,
          None,
          None,
          None,
          None,
      )
  
imports: import math
  from functools import lru_cache
  import torch
  from torch import nn
  from torch.autograd import Function
  from torch.autograd.function import once_differentiable
  from torch.nn.modules.utils import _pair
  from torchvision.ops import deform_conv2d
  from detectron2.utils.develop import create_dummy_class, create_dummy_func
  from .wrappers import _NewEmptyTensorOp
label_desc: Use image node_id 2 for calling the _ModulatedDeformConv obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_input,  grad_offset,  grad_mask,  grad_weight,  grad_bias,  None,  None,  None,  None,  None