class_docstr: GNNModel 
docstr: forward 
docstr_tok: ["forward"]
code: def forward(self):
      """forward"""
      hcl_logits_list = None
  
      (
          id_embedding,
          slot_embedding_list,
      ) = model_util.get_sparse_embedding(
          self.config,
          self.nodeid_slot_holder,
          self.discrete_slot_holders,
          self.discrete_slot_lod_holders,
          self.show_clk,
          self._use_cvm,
          self.emb_size,
      )
  
      # merge id_embedding and slot_embedding_list here
      feature = paddle.add_n([id_embedding] + slot_embedding_list)
      empty_flag = paddle.cast(
          paddle.mean(feature**2, -1, keepdim=True) > 1e-3,
          dtype="float32",
      )
      if self.config.softsign:
          log.info("using softsign in feature_mode (sum)")
          feature = paddle.nn.functional.softsign(feature)
  
      if self.config.sage_mode:
          if not self.is_predict:
              graph_holders = model_util.remove_leakage_edges_gnn(
                  self.graph_holders,
                  self.etype_len,
                  self.final_index,
                  empty_flag,
                  self.return_weight,
              )
          else:
              graph_holders = self.graph_holders
  
          feature = self.gnn_model(
              self.graph_holders, feature, self.node_degree
          )
          if (not self.is_predict) and self.config.hcl:
              hcl_logits_list = model_util.hcl(
                  self.config,
                  self.gnn_model.hcl_buffer,
                  self.graph_holders,
                  empty_flag,
              )
          # remove empty feature. Don't train empty nodes
          feature = feature * empty_flag[: paddle.shape(feature)[0]]
          feature = paddle.gather(feature, self.final_index)
  
      feature = paddle.reshape(feature, shape=[-1, 2, self.emb_size])
  
      src_feat = feature[:, 0:1, :]
      dsts_feat_all = [feature[:, 1:, :]]
      for neg in range(self.neg_num):
          dsts_feat_all.append(
              F.contrib.layers.shuffle_batch(dsts_feat_all[0])
          )
      dsts_feat = paddle.concat(dsts_feat_all, axis=1)
  
      logits = paddle.matmul(
          src_feat, dsts_feat, transpose_y=True
      )  # [batch_size, 1, neg_num+1]
      logits = paddle.squeeze(logits, axis=[1])
  
      predictions = {}
      predictions["logits"] = logits  # [B, neg_num + 1]
      predictions["nfeat"] = feature  # [B, 2, d]
      predictions["src_nfeat"] = src_feat  # [B, 1, d]
      if hcl_logits_list is not None:
          predictions["hcl_logits"] = hcl_logits_list
  
      return predictions
  
imports: import os
  import sys
  import math
  import time
  import numpy
  import pickle
  import paddle
  import paddle.fluid.layers
  import paddle.fluid
  import pgl
  from pgl.utils.logger import log
  from .None import layers
  from .None import model_util
  from .None import loss
  import util
  import helper
  from .auto_heter_gnn import AutoHeterGNN
label_desc: Use image node_id 3 for calling the GNNModel obj's underlying member method code with example usage: obj.forward() and returns: predictions