code: def __init__(
      self, g, meta_path_patterns, in_size, out_size, num_heads, dropout
  ):
      super(TAHIN, self).__init__()
  
      # embeddings for different types of nodes, h0
      self.initializer = nn.init.xavier_uniform_
      self.feature_dict = nn.ParameterDict(
          {
              ntype: nn.Parameter(
                  self.initializer(
                      torch.empty(g.num_nodes(ntype), in_size)
                  )
              )
              for ntype in g.ntypes
          }
      )
  
      # relational neighbor aggregation, this produces h1
      self.RelationalAGG = RelationalAGG(g, in_size, out_size)
  
      # metapath-based aggregation modules for user and item, this produces h2
      self.meta_path_patterns = meta_path_patterns
      # one HANLayer for user, one HANLayer for item
      self.hans = nn.ModuleDict(
          {
              key: HANLayer(
                  value, in_size, out_size, num_heads, dropout
              )
              for key, value in self.meta_path_patterns.items()
          }
      )
  
      # layers to combine h0, h1, and h2
      # used to update node embeddings
      self.user_layer1 = nn.Linear(
          (num_heads + 1) * out_size, out_size, bias=True
      )
      self.user_layer2 = nn.Linear(2 * out_size, out_size, bias=True)
      self.item_layer1 = nn.Linear(
          (num_heads + 1) * out_size, out_size, bias=True
      )
      self.item_layer2 = nn.Linear(2 * out_size, out_size, bias=True)
  
      # layernorm
      self.layernorm = nn.LayerNorm(out_size)
  
      # network to score the node pairs
      self.pred = nn.Linear(out_size, out_size)
      self.dropout = nn.Dropout(dropout)
      self.fc = nn.Linear(out_size, 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 1 to create a new TAHIN object from inherited base classes: nn with example: obj = TAHIN(g, meta_path_patterns, in_size, out_size, num_heads, dropout)