code: def forward(
      ctx,
      input,
      offset,
      weight,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      deformable_groups=1,
      im2col_step=64,
  ):
      if input is not None and input.dim() != 4:
          raise ValueError(
              "Expected 4D tensor as input, got {}D tensor instead.".format(
                  input.dim()
              )
          )
      ctx.stride = _pair(stride)
      ctx.padding = _pair(padding)
      ctx.dilation = _pair(dilation)
      ctx.groups = groups
      ctx.deformable_groups = deformable_groups
      ctx.im2col_step = im2col_step
  
      ctx.save_for_backward(input, offset, weight)
  
      output = input.new_empty(
          _DeformConv._output_size(
              input, weight, ctx.padding, ctx.dilation, ctx.stride
          )
      )
  
      ctx.bufs_ = [
          input.new_empty(0),
          input.new_empty(0),
      ]  # columns, ones
  
      if not input.is_cuda:
          # TODO: let torchvision support full features of our deformconv.
          if deformable_groups != 1:
              raise NotImplementedError(
                  "Deformable Conv with deformable_groups != 1 is not supported on CPUs!"
              )
          return deform_conv2d(
              input,
              offset,
              weight,
              stride=stride,
              padding=padding,
              dilation=dilation,
          )
      else:
          cur_im2col_step = _DeformConv._cal_im2col_step(
              input.shape[0], ctx.im2col_step
          )
          assert (
              input.shape[0] % cur_im2col_step
          ) == 0, "im2col step must divide batchsize"
  
          _C.deform_conv_forward(
              input,
              weight,
              offset,
              output,
              ctx.bufs_[0],
              ctx.bufs_[1],
              weight.size(3),
              weight.size(2),
              ctx.stride[1],
              ctx.stride[0],
              ctx.padding[1],
              ctx.padding[0],
              ctx.dilation[1],
              ctx.dilation[0],
              ctx.groups,
              ctx.deformable_groups,
              cur_im2col_step,
          )
      return output
  
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 1 for calling the _DeformConv obj's underlying member method code with example usage: obj.forward(ctx, input, offset, weight, stride, padding, dilation, groups, deformable_groups, im2col_step) and returns: output, deform_conv2d