code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      x1 = self.layers(x)
      if x1.shape[-1] != x.shape[-1]:
          trim = (x.shape[-1] - x1.shape[-1]) // 2
          x = x[:, :, trim:-trim, trim:-trim]
  
      x = torch.cat([x, x1], 1)
  
      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 _DenseLayerDecoder obj's underlying member method code with example usage: obj.forward(x) and returns: x