code: def test():
      # Train classifier on training set:
      x, y = encode(train_loader)
  
      clf = MultiOutputClassifier(
          SGDClassifier(loss="log", penalty="l2")
      )
      clf.fit(x, y)
  
      train_f1 = f1_score(y, clf.predict(x), average="micro")
  
      # Evaluate on validation set:
      x, y = encode(val_loader)
      val_f1 = f1_score(y, clf.predict(x), average="micro")
  
      # Evaluate on test set:
      x, y = encode(test_loader)
      test_f1 = f1_score(y, clf.predict(x), average="micro")
  
      return train_f1, val_f1, test_f1
  
file_path: pytorch_geometric/examples/graph_sage_unsup_ppi.py
imports: import os.path
  import time
  import torch
  import torch.nn.functional
  import tqdm
  from sklearn.linear_model import SGDClassifier
  from sklearn.metrics import f1_score
  from sklearn.multioutput import MultiOutputClassifier
  from torch_geometric.data import Batch
  from torch_geometric.datasets import PPI
  from torch_geometric.loader import DataLoader, LinkNeighborLoader
  from torch_geometric.nn import GraphSAGE
label_desc: Use image node_id 3 for calling a global function with example usage: test() and returns: train_f1,  val_f1,  test_f1