code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      output_size = [size * 2 for size in x.shape[2:]]
      deconved = self.deconv(x)
      resized = F.interpolate(
          x,
          output_size,
          mode=self.mode,
          align_corners=self.align_corners,
      )
      resized = torch.sum(
          torch.stack(
              resized.split(split_size=resized.shape[1] // 2, dim=1),
              dim=-1,
          ),
          dim=-1,
      )
      out: torch.Tensor = deconved + resized
      return out
  
imports: from __future__ import annotations
  import torch
  from torch import nn
  from torch.nn import functional
  from monai.networks.blocks.regunet_block import RegistrationDownSampleBlock, RegistrationExtractionBlock, RegistrationResidualConvBlock, get_conv_block, get_deconv_block
  from monai.networks.utils import meshgrid_ij
label_desc: Use image node_id 2 for calling the AdditiveUpSampleBlock obj's underlying member method code with example usage: obj.forward(x) and returns: out