code: def __init__(
      self,
      in_channels: int = 3,
      mid_channels: int = 12,
      out_channels: int = 3,
  ):
      super().__init__()
  
      self.rebnconvin = REBNCONV(
          in_channels, out_channels, dilation_rate=1
      )
  
      self.rebnconv1 = REBNCONV(
          out_channels, mid_channels, dilation_rate=1
      )
      self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
      self.rebnconv2 = REBNCONV(
          mid_channels, mid_channels, dilation_rate=1
      )
      self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
  
      self.rebnconv3 = REBNCONV(
          mid_channels, mid_channels, dilation_rate=1
      )
  
      self.rebnconv4 = REBNCONV(
          mid_channels, mid_channels, dilation_rate=2
      )
  
      self.rebnconv3d = REBNCONV(
          mid_channels * 2, mid_channels, dilation_rate=1
      )
      self.rebnconv2d = REBNCONV(
          mid_channels * 2, mid_channels, dilation_rate=1
      )
      self.rebnconv1d = REBNCONV(
          mid_channels * 2, out_channels, dilation_rate=1
      )
  
imports: import torch
  import numpy
  import torch.nn
  import torch.nn.functional
  from PIL import Image
  from torch import Tensor
  from cftool.cv import to_rgb
  from torchvision.transforms.functional import normalize
  from ....misc.toolkit import download_model
label_desc: Use image node_id 1 to create a new RSU4 object from inherited base classes: nn with example: obj = RSU4(in_channels, mid_channels, out_channels)