code: def forward(self, feats: List[torch.Tensor]) -> torch.Tensor:
      out = feats[-1]
      for decoder, fmap in zip(self.decoders[::-1], feats[:-1][::-1]):
          out = decoder(out) + fmap
  
      out = self.decoders[0](out)
  
      return out
  
imports: from typing import Any, Callable, Dict, List, Optional, Tuple
  import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  from torchvision.models._utils import IntermediateLayerGetter
  from doctr.file_utils import CLASS_NAME
  from doctr.models.classification import resnet18, resnet34, resnet50
  from ...utils import _bf16_to_float32, load_pretrained_params
  from .base import LinkNetPostProcessor, _LinkNet
label_desc: Use image node_id 3 for calling the LinkNetFPN obj's underlying member method code with example usage: obj.forward(feats) and returns: out