code: def forward(self, graph, node_feat, edge_feat=None):
      if self.embed_size > 0:
          dgl_warning(
              "The embedding for node feature is used, and input node_feat is ignored, due to the provided embed_size."
          )
          h = self.embed.weight
      else:
          h = node_feat
      for l in range(self.num_layers - 1):
          h = self.gat_layers[l](graph, h).flatten(1)
      # output projection
      logits = self.gat_layers[-1](graph, h).mean(1)
      return logits
  
file_path: dgl/dglgo/dglgo/model/node_encoder/gat.py
imports: from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.base import dgl_warning
  from dgl.nn import GATConv
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.forward(graph, node_feat, edge_feat) and returns: logits