code: def forward(self, x, content_features, style_features):
      content_gamma, content_beta = self.c_gamma(
          content_features
      ), self.c_beta(content_features)
      style_gamma, style_beta = self.s_gamma(
          style_features
      ), self.s_beta(style_features)
  
      # w_gamma_ = nn.clip(self.w_gamma, 0, 1)
      # w_beta_ = nn.clip(self.w_beta, 0, 1)
  
      w_gamma_, w_beta_ = self.w_gamma.expand(
          [x.shape[0], -1]
      ), self.w_beta.expand([x.shape[0], -1])
      soft_gamma = (
          1.0 - w_gamma_
      ) * style_gamma + w_gamma_ * content_gamma
      soft_beta = (1.0 - w_beta_) * style_beta + w_beta_ * content_beta
  
      out = self.norm(x, soft_gamma, soft_beta)
      return out
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 2 for calling the SoftAdaLIN obj's underlying member method code with example usage: obj.forward(x, content_features, style_features) and returns: out