code: def forward(self, x, offset):
      if x.numel() == 0:
          # When input is empty, we want to return a empty tensor with "correct" shape,
          # So that the following operations will not panic
          # if they check for the shape of the tensor.
          # This computes the height and width of the output tensor
          output_shape = [
              (i + 2 * p - (di * (k - 1) + 1)) // s + 1
              for i, p, di, k, s in zip(
                  x.shape[-2:],
                  self.padding,
                  self.dilation,
                  self.kernel_size,
                  self.stride,
              )
          ]
          output_shape = [
              x.shape[0],
              self.weight.shape[0],
          ] + output_shape
          return _NewEmptyTensorOp.apply(x, output_shape)
  
      x = deform_conv(
          x,
          offset,
          self.weight,
          self.stride,
          self.padding,
          self.dilation,
          self.groups,
          self.deformable_groups,
      )
      if self.norm is not None:
          x = self.norm(x)
      if self.activation is not None:
          x = self.activation(x)
      return x
  
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 DeformConv obj's underlying member method code with example usage: obj.forward(x, offset) and returns: x, _NewEmptyTensorOp