code: def forward(self, data):
      x, edge_index, edge_weight = (
          data.x,
          data.edge_index,
          data.edge_attr,
      )
      x = F.relu(self.conv1(x, edge_index, edge_weight))
      x = F.dropout(x, training=self.training)
      x = self.conv2(x, edge_index, edge_weight)
      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 GCN obj's underlying member method code with example usage: obj.forward(data) and returns: F