code: def __init__(self, in_channels, out_channels):
      super(UnetEncoder, self).__init__()
      self.in_channels = in_channels
      self.out_channels = out_channels
  
      self.layer = nn.Sequential(
          nn.Conv2d(
              self.in_channels,
              self.out_channels,
              kernel_size=3,
              padding=1,
          ),
          nn.BatchNorm2d(self.out_channels),
          nn.ReLU(),
          nn.Conv2d(
              self.out_channels,
              self.out_channels,
              kernel_size=3,
              padding=1,
          ),
          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 UnetEncoder object from inherited base classes: nn with example: obj = UnetEncoder(in_channels, out_channels)