code: def test():
      train_emb = model.Z[data["movie"].train_mask].cpu()
      val_emb = model.Z[data["movie"].val_mask].cpu()
      test_emb = model.Z[data["movie"].test_mask].cpu()
  
      train_y = data["movie"].y[data["movie"].train_mask].cpu()
      val_y = data["movie"].y[data["movie"].val_mask].cpu()
      test_y = data["movie"].y[data["movie"].test_mask].cpu()
  
      clf = LogisticRegression().fit(train_emb, train_y)
      return clf.score(val_emb, val_y), clf.score(test_emb, test_y)
  
file_path: pytorch_geometric/examples/hetero/dmgi_unsup.py
imports: import os.path
  import torch
  import torch.nn.functional
  from sklearn.linear_model import LogisticRegression
  from torch.optim import Adam
  import torch_geometric.transforms
  from torch_geometric.datasets import IMDB
  from torch_geometric.nn import GCNConv
label_desc: Use image node_id 2 for calling a global function with example usage: test() and returns: clf,  clf