code: def __init__(self, features, supervision_channels: int):
      super().__init__()
      self.squeeze = nn.Conv2d(
          features, supervision_channels, kernel_size=1
      )
      self.expand = nn.Conv2d(
          supervision_channels, features, kernel_size=1
      )
  
imports: import inspect
  from collections import OrderedDict
  from typing import List, Callable, Tuple
  import torch
  from pytorch_toolbelt.modules import ACT_RELU, get_activation_block
  from pytorch_toolbelt.modules.encoders import EncoderModule, make_n_channel_input
  from torch import nn, Tensor
label_desc: Use image node_id 1 to create a new HGSupervisionBlock object from inherited base classes: nn with example: obj = HGSupervisionBlock(features, supervision_channels)