code: import openml
  from pprint import pprint
  from autoPyTorch import AutoNetClassification
  from sklearn.metrics import accuracy_score
  
  
  # get OpenML task by its ID
  task = openml.tasks.get_task(task_id=32)
  X, y = task.get_X_and_y()
  ind_train, ind_test = task.get_train_test_split_indices()
  
  
  # run Auto-PyTorch
  autoPyTorch = AutoNetClassification("tiny_cs",  # config preset
                                      log_level='info',
                                      max_runtime=300,
                                      min_budget=30,
                                      max_budget=90)
  
  autoPyTorch.fit(X[ind_train], y[ind_train], validation_split=0.3)
  
  
  # predict
  y_pred = autoPyTorch.predict(X[ind_test])
  
  print("Accuracy score", accuracy_score(y[ind_test], y_pred))
  
  
  # print network configuration
  pprint(autoPyTorch.fit_result["optimized_hyperparameter_config"])
  
apis: sklearn.metrics.accuracy_score