code: def forward(self, inputs):
      h = inputs
      for l in range(self.num_layers):
          h = self.gat_layers[l](self.g, h).flatten()
          h = self.activation(h)
      # output projection
      logits = self.gat_layers[-1](self.g, h).mean(1)
      return logits
  
imports: import mxnet.gluon.nn
  from dgl.nn.mxnet.conv import GATConv
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.forward(inputs) and returns: logits