code: def forward(self, A_list, X):
      meta_path_Z_list = []
      for i, A in enumerate(A_list):
          meta_path_Z_list.append(self.gat_layers[i](A, X).flatten(1))
  
      # (num_nodes, num_meta_paths, hidden_size * num_heads)
      meta_path_Z = torch.stack(meta_path_Z_list, dim=1)
  
      Z = self.semantic_attention(meta_path_Z)
      Z = self.predict(Z)
  
      return Z
  
imports: import pickle
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.data.utils import _get_dgl_url, download, get_download_dir
  from torch.optim import Adam
label_desc: Use image node_id 2 for calling the HAN obj's underlying member method code with example usage: obj.forward(A_list, X) and returns: Z