code: def forward(
      self, xin: torch.Tensor, short_cuts: list[torch.Tensor]
  ) -> torch.Tensor:
      block_number = len(short_cuts) - 1
      x = xin + short_cuts[block_number]
  
      for block in self.decoder_blocks:
          x = block(x)
          x = self.upsample(x)
          block_number -= 1
          trim = (short_cuts[block_number].shape[-1] - x.shape[-1]) // 2
          if trim > 0:
              x += short_cuts[block_number][
                  :, :, trim:-trim, trim:-trim
              ]
  
      for block in self.output_features:
          x = block(x)
  
      return x
  
imports: from __future__ import annotations
  import os
  import re
  import warnings
  from collections import OrderedDict
  from collections.abc import Callable, Sequence
  import torch
  import torch.nn
  from monai.apps.utils import download_url
  from monai.networks.blocks import UpSample
  from monai.networks.layers.factories import Conv, Dropout
  from monai.networks.layers.utils import get_act_layer, get_norm_layer
  from monai.utils.enums import HoVerNetBranch, HoVerNetMode, InterpolateMode, UpsampleMode
  from monai.utils.module import export, look_up_option
label_desc: Use image node_id 2 for calling the _DecoderBranch obj's underlying member method code with example usage: obj.forward(xin, short_cuts) and returns: x