code: def forward(self, g, inputs):
      h = inputs
      for l in range(self.num_layers):
          h = self.gat_layers[l](g, h).flatten(1)
      # output projection
      logits = self.gat_layers[-1](g, h).mean(1)
      return logits
  
file_path: dgl/benchmarks/benchmarks/model_acc/bench_gat.py
imports: import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GATConv
  from ..None import utils
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.forward(g, inputs) and returns: logits