class_docstr: Deep supervision head takes all feature maps and applies 1x1 convolution to each of them.
code: def apply_to_final_layer(self, func: Callable[[nn.Module], None]):
      for head in self.heads:
          func(head)
  
file_path: pytorch-toolbelt/pytorch_toolbelt/modules/heads/deep_supervision.py
imports: from typing import List, Dict, Optional, Callable
  import torch
  import torch.nn
  from pytorch_toolbelt.datasets import name_for_stride
  from pytorch_toolbelt.modules.simple import conv1x1
  from pytorch_toolbelt.modules.interfaces import AbstractHead, FeatureMapsSpecification
label_desc: Use image node_id 4 for calling the DeepSupervisionHead obj's underlying member method code with example usage: obj.apply_to_final_layer(func) without return types