code: def backward(ctx, grad_output):
      if not grad_output.is_cuda:
          raise NotImplementedError(
              "DCN operator for CPU for backward propagation is not implemented."
          )
      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)
      deform_conv_cuda.modulated_deform_conv_cuda_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,
      )
  
file_path: EasyOCR/easyocr/DBNet/assets/ops/dcn/functions/deform_conv.py
imports: import os
  import torch
  import warnings
  from torch.autograd import Function
  from torch.nn.modules.utils import _pair
  from torch.utils import cpp_extension
label_desc: Use image node_id 2 for calling the ModulatedDeformConvFunction 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