code: def forward(self, features):
      (
          num_nodes,
          num_edges,
          edges,
          node_feat_index,
          node_feat_term_ids,
          user_index,
          pos_item_index,
          neg_item_index,
          user_real_index,
          pos_item_real_index,
      ) = features
  
      node_feat = {
          "index": node_feat_index,
          "term_ids": node_feat_term_ids,
      }
      graph_wrapper = BatchGraphWrapper(
          num_nodes, num_edges, edges, node_feat
      )
  
      encoder = Encoder.factory(self.hparam)
      outputs = encoder(
          [graph_wrapper], [user_index, pos_item_index, neg_item_index]
      )
      user_feat, pos_item_feat, neg_item_feat = outputs
  
      # loss
      if self.hparam.neg_type == "batch_neg":
          neg_item_feat = pos_item_feat
  
      if self.mode is propeller.RunMode.TRAIN:
          return user_feat, pos_item_feat, neg_item_feat
      elif self.mode is propeller.RunMode.PREDICT:
          return user_feat, user_real_index
  
      elif self.mode is propeller.RunMode.EVAL:
          return user_feat, pos_item_feat, neg_item_feat
  
imports: import os
  import argparse
  import traceback
  import re
  import io
  import yaml
  import numpy
  from easydict import EasyDict
  from pgl.utils import paddle_helper
  from pgl.graph_wrapper import BatchGraphWrapper
  import propeller.paddle
  from propeller.paddle.data import Dataset
  import paddle.fluid
  import paddle.fluid.layers
  import logging
  from propeller import log
  from ernie.tokenizing_ernie import ErnieTokenizer
  from ernie.tokenizing_ernie import ErnieTinyTokenizer
  from dataset.graph_reader import BatchGraphGenerator
  from models.encoder import Encoder
  from models.pretrain_model_loader import PretrainedModelLoader
  from models.loss import Loss
  from optimization import optimization
label_desc: Use image node_id 2 for calling the ERNIESageLinkPredictModel obj's underlying member method code with example usage: obj.forward(features) and returns: user_feat,  pos_item_feat,  neg_item_feat, user_feat,  user_real_index, user_feat,  pos_item_feat,  neg_item_feat