code: def forward(self, g):
      h = g.nodes[self.args["out_key"]].data["feat"]
      for gnn in self.layers:
          h = gnn(g, h)
  
      return self.predict(h)
  
imports: import logging
  import dgl
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  import dgl.function
  from dgl.nn.functional import edge_softmax
  from dgl.nn.pytorch import GATConv
  from functools import partial
  from ..._utils import activation
  from ..None import register_model
  from .base import BaseHeteroModelMaintainer
  from .....utils import get_logger
label_desc: Use image node_id 2 for calling the HAN obj's underlying member method code with example usage: obj.forward(g) and returns: self