code: def __init__(self, in_channels, out_channels):
      super(UNetDec, self).__init__()
  
      self.up = nn.Sequential(
          nn.Conv2d(
              in_channels, out_channels, 3, padding=1, bias=False
          ),
          nn.BatchNorm2d(out_channels),
          nn.ReLU(inplace=True),
          nn.Conv2d(
              out_channels, out_channels, 3, padding=1, bias=False
          ),
          nn.ReLU(inplace=True),
          nn.BatchNorm2d(out_channels),
          nn.ConvTranspose2d(out_channels, out_channels, 2, stride=2),
          nn.ReLU(inplace=True),
      )
  
      self.SE1 = nn.Conv2d(
          in_channels, in_channels // 16, kernel_size=1
      )
      self.SE2 = nn.Conv2d(
          in_channels // 16, in_channels, kernel_size=1
      )
  
file_path: pywick/pywick/models/segmentation/testnets/unet_se.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new UNetDec object from inherited base classes: nn with example: obj = UNetDec(in_channels, out_channels)