code: def label_embed_input(self, feature):
      label = F.data(name="label", shape=[None, 1], dtype="int64")
      label_idx = F.data(name="label_idx", shape=[None], dtype="int64")
      label = L.reshape(label, shape=[-1])
      label = L.gather(label, label_idx, overwrite=False)
  
      embed_attr = F.ParamAttr(
          initializer=F.initializer.NormalInitializer(
              loc=0.0, scale=1.0
          )
      )
      embed = F.embedding(
          input=label,
          size=(self.out_size, self.embed_size),
          param_attr=embed_attr,
      )
      feature_label = L.gather(feature, label_idx, overwrite=False)
      feature_label = feature_label + embed
      feature = L.scatter(
          feature, label_idx, feature_label, overwrite=True
      )
  
      lay_norm_attr = F.ParamAttr(
          initializer=F.initializer.ConstantInitializer(value=1)
      )
      lay_norm_bias = F.ParamAttr(
          initializer=F.initializer.ConstantInitializer(value=0)
      )
      feature = L.layer_norm(
          feature,
          name="layer_norm_feature_input",
          param_attr=lay_norm_attr,
          bias_attr=lay_norm_bias,
      )
  
      return feature
  
imports: import math
  import paddle.fluid
  import paddle.fluid.layers
  from module.transformer_gat_pgl import transformer_gat_pgl
label_desc: Use image node_id 2 for calling the Arxiv_label_embedding_model obj's underlying member method code with example usage: obj.label_embed_input(feature) and returns: feature