code: def ogc() -> float:
      U = data.x
      _, _, last_acc, last_pred = model.test(U, y_one_hot, data)
  
      patience = 0
      for i in range(1, 65):
          # Updating W by training a simple linear neural network:
          pred, W = model.update_W(U, y_one_hot, data)
  
          # Updating U by LGC and SEB jointly:
          U = update_U(U, y_one_hot, pred, W)
  
          loss, trainval_acc, test_acc, pred = model.test(
              U, y_one_hot, data
          )
          print(
              f"Epoch: {i:02d}, Loss: {loss:.4f}, "
              f"Train+Val Acc: {trainval_acc:.4f} Test Acc {test_acc:.4f}"
          )
  
          sim_rate = float((pred == last_pred).sum()) / pred.size(0)
          if sim_rate > max_sim_tol:
              patience += 1
              if patience > max_patience:
                  break
  
          last_acc, last_pred = test_acc, pred
  
      return last_acc
  
file_path: pytorch_geometric/examples/ogc.py
imports: import argparse
  import os.path
  import time
  import warnings
  import torch
  import torch.nn.functional
  from torch import Tensor
  import torch_geometric.transforms
  from torch_geometric.data import Data
  from torch_geometric.datasets import Planetoid
  from torch_geometric.utils import one_hot
label_desc: Use image node_id 2 for calling a global function with example usage: ogc() and returns: last_acc