class_docstr: Hierarchical feature fusion module.
  https://arxiv.org/pdf/1811.11431.pdf
  https://arxiv.org/pdf/1803.06815.pdf
  
  What it does is easily explained in code:
  feature_map_0 - feature_map of the highest resolution
  feature_map_N - feature_map of the smallest resolution
  
  >>> feature_map = feature_map_0 + up(feature_map[1] + up(feature_map[2] + up(feature_map[3] + ...))))
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code: def __init__(
      self,
      sizes=None,
      upsample_scale=2,
      mode="nearest",
      align_corners=None,
  ):
      super().__init__()
      self.sizes = sizes
      self.interpolation_mode = mode
      self.align_corners = align_corners
      self.upsample_scale = upsample_scale
  
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 1 to create a new HFF object from inherited base classes: nn with example: obj = HFF(sizes, upsample_scale, mode, align_corners)