class_docstr: BiFPN decoder. Note this class does not compute additional feature maps (p6/p7) and expects them to be present in input.
  Because of this it supports arbitrary number of input feature maps.
  
  Reference: https://arxiv.org/abs/1911.09070
class_docstr_tok: ["BiFPN","decoder",".","Note","this","class","does","not","compute","additional","feature","maps","(","p6\/p7",")","and","expects","them","to","be","present","in","input",".","Because","of","this","it","supports","arbitrary","number","of","input","feature","maps",".","Reference",":","https",":","\/\/arxiv.org\/abs\/1911.09070"]
code: def get_output_spec(self) -> FeatureMapsSpecification:
      return self.output_spec
  
imports: from typing import List, Tuple, Union, Type, Callable
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from pytorch_toolbelt.modules.activations import ACT_RELU, instantiate_activation_block
  from pytorch_toolbelt.modules.simple import conv1x1
  from pytorch_toolbelt.modules.dsconv import DepthwiseSeparableConv2dBlock
  from pytorch_toolbelt.modules.interfaces import AbstractDecoder, FeatureMapsSpecification
  from pytorch_toolbelt.modules.normalization import NORM_BATCH, instantiate_normalization_block
label_desc: Use image node_id 3 for calling the BiFPNDecoder obj's underlying member method code with example usage: obj.get_output_spec() and returns: self