code: def test_predict_wrong_features(toy_data, n_features):
      X, y = toy_data
      tree = SurvivalTree(max_depth=1)
      tree.fit(X, y)
  
      X_new = np.random.randn(12, n_features)
      with pytest.raises(
          ValueError,
          match=f"X has {n_features} features, but SurvivalTree is expecting 4 features as input.",
      ):
          tree.predict(X_new)
  
imports: from itertools import product
  from queue import LifoQueue
  import numpy
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  import pandas
  import pytest
  from scipy import sparse
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import OrdinalEncoder
  from sklearn.tree._tree import TREE_UNDEFINED
  from sksurv.compare import compare_survival
  from sksurv.datasets import load_breast_cancer, load_veterans_lung_cancer
  from sksurv.nonparametric import kaplan_meier_estimator, nelson_aalen_estimator
  from sksurv.tree import SurvivalTree
  from sksurv.util import Surv
label_desc: Use image node_id 16 for calling a global function with example usage: test_predict_wrong_features(toy_data, n_features) without return types