class_docstr: Compute a sum of individual FPN layers
class_docstr_tok: ["Compute","a","sum","of","individual","FPN","layers"]
code: def forward(
      self, features: List[Tensor]
  ) -> Tensor:  # skipcq: PYL-W0221
      output = features[0]
      dst_size = features[0].size()[
          2:
      ]  # Skip B, C, and use rest (This works for 1D, 2D, 3D and ND..)
  
      for f in features[1:]:
          output = output + F.interpolate(
              f,
              size=dst_size,
              mode=self.mode,
              align_corners=self.align_corners,
          )
  
      return output
  
imports: from __future__ import absolute_import
  from typing import List
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from ..modules.activations import ABN
label_desc: Use image node_id 2 for calling the FPNFuseSum obj's underlying member method code with example usage: obj.forward(features) and returns: output