code: def get_batch_feature(self, feature):
      batch_nodes = F.data(
          name="batch_nodes_0", shape=[None], dtype="int64"
      )
      #         feature=L.index_select(feature, batch_nodes)
      feature = L.gather(feature, batch_nodes, overwrite=False)
  
      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,
      )
  
      #         feature=L.dropout(feature, dropout_prob=self.dropout, dropout_implementation='upscale_in_train',
      #                          is_test=test)
      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 3 for calling the Products_label_embedding_model obj's underlying member method code with example usage: obj.get_batch_feature(feature) and returns: feature