code: def __init__(self, g, in_size, out_size, dropout=0.1):
      super(RelationalAGG, self).__init__()
      self.in_size = in_size
      self.out_size = out_size
  
      # Transform weights for different types of edges
      self.W_T = nn.ModuleDict(
          {
              name: nn.Linear(in_size, out_size, bias=False)
              for name in g.etypes
          }
      )
  
      # Attention weights for different types of edges
      self.W_A = nn.ModuleDict(
          {
              name: nn.Linear(out_size, 1, bias=False)
              for name in g.etypes
          }
      )
  
      # layernorm
      self.layernorm = nn.LayerNorm(out_size)
  
      # dropout layer
      self.dropout = nn.Dropout(dropout)
  
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 RelationalAGG object from inherited base classes: nn with example: obj = RelationalAGG(g, in_size, out_size, dropout)