code: def __init__(self, in_channels, featrures, out_channels):
      super(UnetDecoder, self).__init__()
      self.in_channels = in_channels
      self.features = featrures
      self.out_channels = out_channels
  
      self.layer = nn.Sequential(
          nn.Conv2d(
              self.in_channels, self.features, kernel_size=3, padding=1
          ),
          nn.BatchNorm2d(self.features),
          nn.ReLU(),
          nn.Conv2d(
              self.features, self.features, kernel_size=3, padding=1
          ),
          nn.BatchNorm2d(self.features),
          nn.ReLU(),
          nn.ConvTranspose2d(
              self.features, self.out_channels, kernel_size=2, stride=2
          ),
          nn.BatchNorm2d(self.out_channels),
          nn.ReLU(),
      )
  
imports: import torch
  import torch.nn
  import torch.nn.init
  import torch.nn.functional
  import numpy
label_desc: Use image node_id 1 to create a new UnetDecoder object from inherited base classes: nn with example: obj = UnetDecoder(in_channels, featrures, out_channels)