code: def _output_size(input, weight, padding, dilation, stride):
      channels = weight.size(0)
      output_size = (input.size(0), channels)
      for d in range(input.dim() - 2):
          in_size = input.size(d + 2)
          pad = padding[d]
          kernel = dilation[d] * (weight.size(d + 2) - 1) + 1
          stride_ = stride[d]
          output_size += (
              (in_size + (2 * pad) - kernel) // stride_ + 1,
          )
      if not all(map(lambda s: s > 0, output_size)):
          raise ValueError(
              "convolution input is too small (output would be {})".format(
                  "x".join(map(str, output_size))
              )
          )
      return output_size
  
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 3 for calling the _DeformConv obj's underlying member method code with example usage: obj._output_size(input, weight, padding, dilation, stride) and returns: output_size