code: def encode(self, data):
      x, edge_index = data.x, data.train_pos_edge_index
      x = F.relu(self.conv1(x, edge_index))
      x = self.conv2(x, edge_index)
      return x
  
imports: import time
  import torch
  import torch.nn.functional
  import numpy
  import random
  from torch_geometric.datasets import Planetoid
  from torch_geometric.nn import GCNConv, GATConv, SAGEConv
  import torch_geometric.transforms
  from torch_geometric.utils import train_test_split_edges
  from torch_geometric.utils import negative_sampling
  import os.path
  from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
  from tqdm import tqdm
  from sklearn.metrics import roc_auc_score
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.encode(data) and returns: x