code: def _output_size(ctx, input, weight):
      channels = weight.size(0)
      output_size = (input.size(0), channels)
      for d in range(input.dim() - 2):
          in_size = input.size(d + 2)
          pad = ctx.padding[d]
          kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1
          stride_ = ctx.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 "
              + "x".join(map(str, output_size))
              + ")"
          )
      return output_size
  
imports: import math
  import torch
  import torch.nn
  from torch.autograd import Function
  from torch.autograd.function import once_differentiable
  from torch.nn.modules.utils import _pair, _single
  from annotator.uniformer.mmcv.utils import deprecated_api_warning
  from ..cnn import CONV_LAYERS
  from ..utils import ext_loader, print_log
label_desc: Use image node_id 4 for calling the ModulatedDeformConv2dFunction obj's underlying member method code with example usage: obj._output_size(ctx, input, weight) and returns: output_size