code: def _make_trunk_output_block(self, num_planes, final_bn_relu):
      layers = nn.Sequential()
      if final_bn_relu:
          # final batch normalization:
          layers.add_module("norm-final", nn.BatchNorm2d(num_planes))
          layers.add_module("relu-final", nn.ReLU(inplace=INPLACE))
      return layers
  
imports: import math
  from collections import OrderedDict
  from typing import Any, Dict, Sequence
  import torch
  import torch.nn
  from classy_vision.generic.util import is_pos_int
  from .None import register_model
  from .classy_model import ClassyModel
  from .squeeze_and_excitation_layer import SqueezeAndExcitationLayer
label_desc: Use image node_id 2 for calling the DenseNet obj's underlying member method code with example usage: obj._make_trunk_output_block(num_planes, final_bn_relu) and returns: layers