code: def __init__(self, num_features, eps=1e-5):
      super(SoftAdaLIN, self).__init__()
      self.norm = AdaLIN(num_features, eps)
  
      self.w_gamma = self.create_parameter(
          [1, num_features],
          default_initializer=nn.initializer.Constant(0.0),
      )
      self.w_beta = self.create_parameter(
          [1, num_features],
          default_initializer=nn.initializer.Constant(0.0),
      )
  
      self.c_gamma = nn.Sequential(
          nn.Linear(num_features, num_features, bias_attr=False),
          nn.ReLU(),
          nn.Linear(num_features, num_features, bias_attr=False),
      )
      self.c_beta = nn.Sequential(
          nn.Linear(num_features, num_features, bias_attr=False),
          nn.ReLU(),
          nn.Linear(num_features, num_features, bias_attr=False),
      )
      self.s_gamma = nn.Linear(
          num_features, num_features, bias_attr=False
      )
      self.s_beta = nn.Linear(
          num_features, num_features, bias_attr=False
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new SoftAdaLIN object from inherited base classes: nn with example: obj = SoftAdaLIN(num_features, eps)