code: def forward(self, x_dict, edge_index_dict, edge_label_index):
      z_dict = {}
      x_dict["user"] = self.user_emb(x_dict["user"])
      z_dict["user"] = self.user_encoder(x_dict, edge_index_dict)
      z_dict["movie"] = self.movie_encoder(
          x_dict["movie"],
          edge_index_dict[("movie", "metapath_0", "movie")],
      )
      return self.decoder(
          z_dict["user"], z_dict["movie"], edge_label_index
      )
  
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Embedding, Linear
  import torch_geometric.transforms
  from torch_geometric.datasets import MovieLens
  from torch_geometric.nn import SAGEConv
  from torch_geometric.nn.conv.gcn_conv import gcn_norm
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj.forward(x_dict, edge_index_dict, edge_label_index) and returns: self