code: def __init__(self, num_features, hidden_features):
      super(GraphSAGE, self).__init__()
      self.num_layers = 2
      self.convs = torch.nn.ModuleList()
      for i in range(self.num_layers):
          inc = outc = hidden_features
          if i == 0:
              inc = num_features
          if i == self.num_layers - 1:
              outc = hidden_features // 2
          self.convs.append(SAGEConv(inc, outc))
  
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 1 to create a new GraphSAGE object from inherited base classes: torch.nn with example: obj = GraphSAGE(num_features, hidden_features)