code: def encode(self, data):
      x, edge_index = data.x, data.train_pos_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 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 GraphSAGE obj's underlying member method code with example usage: obj.encode(data) and returns: x