code: def __init__(self, x_channels, y_channels, kernel_size=3):
      super(StackEncoder, self).__init__()
      padding = (kernel_size - 1) // 2
      self.encode = nn.Sequential(
          ConvBnRelu2d(
              x_channels,
              y_channels,
              kernel_size=kernel_size,
              padding=padding,
              dilation=1,
              stride=1,
              groups=1,
          ),
          ConvBnRelu2d(
              y_channels,
              y_channels,
              kernel_size=kernel_size,
              padding=padding,
              dilation=1,
              stride=1,
              groups=1,
          ),
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new StackEncoder object from inherited base classes: nn with example: obj = StackEncoder(x_channels, y_channels, kernel_size)