class_docstr: An up-sample module that can be used for LocalNet, based on:
  `Weakly-supervised convolutional neural networks for multimodal image registration
  <https://doi.org/10.1016/j.media.2018.07.002>`_.
  `Label-driven weakly-supervised learning for multimodal deformable image registration
  <https://arxiv.org/abs/1711.01666>`_.
  
  Adapted from:
      DeepReg (https://github.com/DeepRegNet/DeepReg)
code: def additive_upsampling(self, x, mid) -> torch.Tensor:
      x = F.interpolate(
          x,
          mid.shape[2:],
          mode=self.mode,
          align_corners=self.align_corners,
      )
      # [(batch, out_channels, ...), (batch, out_channels, ...)]
      x = x.split(split_size=int(self.out_channels), dim=1)
      # (batch, out_channels, ...)
      out: torch.Tensor = torch.sum(torch.stack(x, dim=-1), dim=-1)
      return out
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  from torch import nn
  from torch.nn import functional
  from monai.networks.blocks import Convolution
  from monai.networks.layers import same_padding
  from monai.networks.layers.factories import Conv, Norm, Pool
label_desc: Use image node_id 2 for calling the LocalNetUpSampleBlock obj's underlying member method code with example usage: obj.additive_upsampling(x, mid) and returns: out