code: def __init__(
      self,
      low_feature,
      high_features,
      norm=nn.InstanceNorm2d,
      up_scale=2,
  ):
      super(SEB, self).__init__()
      self.sebs = []
      for c in range(len(high_features) - 1, 0, -1):
          self.sebs.append(
              nn.Sequential(
                  CNA(
                      high_features[c], high_features[c - 1], norm=norm
                  ),
                  nn.UpsamplingBilinear2d(scale_factor=up_scale),
              )
          )
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new SEB object from inherited base classes: nn with example: obj = SEB(low_feature, high_features, norm, up_scale)