code: def __init__(self, in_channels, middle_channels, out_channels):
      super(_DecoderBlock, self).__init__()
      self.decode = nn.Sequential(
          nn.Conv2d(in_channels, middle_channels, kernel_size=3),
          nn.BatchNorm2d(middle_channels),
          nn.ReLU(inplace=True),
          nn.Conv2d(middle_channels, middle_channels, kernel_size=3),
          nn.BatchNorm2d(middle_channels),
          nn.ReLU(inplace=True),
          nn.ConvTranspose2d(
              middle_channels, out_channels, kernel_size=2, stride=2
          ),
      )
  
file_path: pywick/pywick/models/segmentation/u_net.py
imports: import torch
  import torch.nn.functional
  from torch import nn
  from .fcn_utils import initialize_weights
label_desc: Use image node_id 1 to create a new _DecoderBlock object from inherited base classes: nn with example: obj = _DecoderBlock(in_channels, middle_channels, out_channels)