class_docstr: Hypercolumn head that concatenates all input feature maps to the size of the largest feature map, compute
  a projection (using conv + bn + act) and resize the projected feature map to the original image size.
  
  Reference: https://arxiv.org/pdf/1411.5752.pdf
code: def forward(
      self, feature_maps: List[Tensor], output_size: torch.Size
  ):
      x = self.fuse(feature_maps)
      x = self.projection(x)
      x = self.final(x)
  
      output = torch.nn.functional.interpolate(
          x,
          size=output_size,
          mode=self.interpolation_mode,
          align_corners=self.interpolation_align_corners,
      )
  
      if self.output_name is not None:
          return {self.output_name: output}
      else:
          return output
  
file_path: pytorch-toolbelt/pytorch_toolbelt/modules/heads/hypercolumn.py
imports: from typing import Optional, List
  import torch.jit
  from torch import nn, Tensor
  from pytorch_toolbelt.modules import FPNFuse, instantiate_activation_block, instantiate_normalization_block
  from pytorch_toolbelt.modules.interfaces import AbstractHead, FeatureMapsSpecification
label_desc: Use image node_id 2 for calling the HypercolumnHead obj's underlying member method code with example usage: obj.forward(feature_maps, output_size) and returns: dict, output