code: def forward(self, data):
      g = data
      in_feat = data.ndata["feat"]
      h = self.conv1(g, in_feat)
      h = F.relu(h)
      h = self.conv2(g, h)
      return h
  
imports: import pickle
  import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  import scipy.sparse
  import random
  from dgl.data import CoraGraphDataset, PubmedGraphDataset, CiteseerGraphDataset
  import dgl.data
  from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
  from tqdm import tqdm
  from dgl.nn import SAGEConv
  from dgl.nn.pytorch.conv import GraphConv
  from dgl.nn import GATConv
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 2 for calling the GraphSAGE obj's underlying member method code with example usage: obj.forward(data) and returns: h