code: def forward(self, g, user_key, item_key, user_idx, item_idx):
      # relational neighbor aggregation, h1
      h1 = self.RelationalAGG(g, self.feature_dict)
  
      # metapath-based aggregation, h2
      h2 = {}
      for key in self.meta_path_patterns.keys():
          h2[key] = self.hans[key](g, self.feature_dict[key])
  
      # update node embeddings
      user_emb = torch.cat((h1[user_key], h2[user_key]), 1)
      item_emb = torch.cat((h1[item_key], h2[item_key]), 1)
      user_emb = self.user_layer1(user_emb)
      item_emb = self.item_layer1(item_emb)
      user_emb = self.user_layer2(
          torch.cat((user_emb, self.feature_dict[user_key]), 1)
      )
      item_emb = self.item_layer2(
          torch.cat((item_emb, self.feature_dict[item_key]), 1)
      )
  
      # Relu
      user_emb = F.relu_(user_emb)
      item_emb = F.relu_(item_emb)
  
      # layer norm
      user_emb = self.layernorm(user_emb)
      item_emb = self.layernorm(item_emb)
  
      # obtain users/items embeddings and their interactions
      user_feat = user_emb[user_idx]
      item_feat = item_emb[item_idx]
      interaction = user_feat * item_feat
  
      # score the node pairs
      pred = self.pred(interaction)
      pred = self.dropout(pred)  # dropout
      pred = self.fc(pred)
      pred = torch.sigmoid(pred)
  
      return pred.squeeze(1)
  
imports: import dgl
  import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GATConv
label_desc: Use image node_id 2 for calling the TAHIN obj's underlying member method code with example usage: obj.forward(g, user_key, item_key, user_idx, item_idx) and returns: pred