code: def test_evaluate_score_ndim(X, y):
      """Test fit() works for both y.ndim == 1 and y.ndim == 2. Two test cases
      are listed in @pytest.mark.parametrize()
      """
      sr = SimpleRegressor(random_state=0)
      print(f"Data ndim: X: {X.shape}, y: {y.shape}")
      sr.fit(X, y)
  
imports: import pytest
  import pandas
  import numpy
  from sklearn.datasets import load_iris, make_blobs, load_digits, fetch_openml
  from sklearn.svm import LinearSVC
  from sklearn.linear_model import LogisticRegression
  from sklearn.dummy import DummyClassifier
  from dabl.datasets import load_titanic
  from dabl.models import SimpleClassifier, SimpleRegressor, AnyClassifier
label_desc: Use image node_id 11 for calling a global function with example usage: test_evaluate_score_ndim(X, y) without return types