class_docstr: Implements DeepLabV3 model from `"Rethinking Atrous Convolution for Semantic Image Segmentation"
  <https://arxiv.org/abs/1706.05587>`_.
  
  Partially copy-pasted from https://github.com/pytorch/vision/blob/master/torchvision/models/segmentation/deeplabv3.py
code: def forward(self, feature_maps: List[Tensor]) -> List[Tensor]:
      coarse_features = self.aspp(feature_maps[-1])
      low_level_features = self.project(feature_maps[0])
  
      coarse_features_resized = F.interpolate(
          coarse_features,
          size=low_level_features.shape[2:],
          mode="bilinear",
          align_corners=False,
      )
      combined_features = torch.cat(
          [low_level_features, coarse_features_resized], dim=1
      )
      return [self.final(combined_features), coarse_features]
  
file_path: pytorch-toolbelt/pytorch_toolbelt/modules/decoders/deeplab_v3_plus.py
imports: from typing import List
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ..activations import instantiate_activation_block, ACT_RELU
  from ..interfaces import AbstractDecoder, FeatureMapsSpecification
  from ..spp import ASPP, SeparableASPPModule
label_desc: Use image node_id 2 for calling the DeeplabV3PlusDecoder obj's underlying member method code with example usage: obj.forward(feature_maps) and returns: list