code: def forward(self, graph):
      features = graph.ndata["feat"]
      features = self.conv1(graph, features).flatten(1)
      features = self.conv2(graph, features).mean(1)
      return F.log_softmax(features, dim=-1)
  
imports: import numpy
  from tqdm import tqdm
  import pickle
  import torch
  import torch.nn.functional
  from dgl.data import CoraGraphDataset, PubmedGraphDataset, CiteseerGraphDataset
  from dgl.nn.pytorch import GraphConv, GATConv, SAGEConv
  import logging
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.forward(graph) and returns: F