code: def __init__(self, num_features, hidden_features, heads):
      super(GAT, self).__init__()
      self.conv1 = GATConv(
          num_features, hidden_features, heads, dropout=0.0
      )
      self.conv2 = GATConv(
          hidden_features * heads,
          hidden_features,
          heads=8,
          concat=True,
          dropout=0.0,
      )
  
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 GAT object from inherited base classes: torch.nn with example: obj = GAT(num_features, hidden_features, heads)