code: def forward(self, g, inference=False):
      if not isinstance(g, list):
          subgraphs = [g] * self.n_layers
      else:
          subgraphs = g
  
      h = subgraphs[0].srcdata["feat"]
      h = self.input_drop(h)
  
      h_last = None
  
      for i in range(self.n_layers):
          if self.edge_encoder is not None:
              efeat = subgraphs[i].edata["feat"]
              efeat_emb = self.edge_encoder[i](efeat)
              efeat_emb = F.relu(efeat_emb, inplace=True)
          else:
              efeat_emb = None
  
          h = self.convs[i](subgraphs[i], h, efeat_emb).flatten(1, -1)
  
          if self.residual and h_last is not None:
              h += h_last[: h.shape[0], :]
  
          h_last = h
  
          h = self.norms[i](h)
          h = self.activation(h, inplace=True)
          h = self.dropout(h)
  
          if inference:
              torch.cuda.empty_cache()
  
      h = self.pred_linear(h)
  
      return h
  
file_path: dgl/examples/pytorch/ogb/ogbn-products/gat/models.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from dgl import function
  from dgl.ops import edge_softmax
  from dgl.utils import expand_as_pair
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.forward(g, inference) and returns: h