code: def forward(self, data):
      x, edge_index = data.x, data.edge_index
      for i, conv in enumerate(self.convs):
          x = conv(x, edge_index)
          if i != self.num_layers - 1:
              x = x.relu()
              x = F.dropout(x, p=0.5, training=self.training)
      return F.log_softmax(x, dim=-1)
  
imports: import os
  import random
  import numpy
  from tqdm import tqdm
  import pickle
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import Planetoid
  import torch_geometric.transforms
  from torch_geometric.nn import GCNConv, GATConv, SAGEConv
  import logging
label_desc: Use image node_id 2 for calling the SAGE obj's underlying member method code with example usage: obj.forward(data) and returns: F