code: def test_isotonic_2darray_more_than_1_feature():
      # Ensure IsotonicRegression raises error if input has more than 1 feature
      X = np.arange(10)
      X_2d = np.c_[X, X]
      y = np.arange(10)
  
      msg = "should be a 1d array or 2d array with 1 feature"
      with pytest.raises(ValueError, match=msg):
          IsotonicRegression().fit(X_2d, y)
  
      iso_reg = IsotonicRegression().fit(X, y)
      with pytest.raises(ValueError, match=msg):
          iso_reg.predict(X_2d)
  
      with pytest.raises(ValueError, match=msg):
          iso_reg.transform(X_2d)
  
imports: import copy
  import pickle
  import warnings
  import numpy
  import pytest
  from scipy.special import expit
  import sklearn
  from sklearn.datasets import make_regression
  from sklearn.isotonic import IsotonicRegression, _make_unique, check_increasing, isotonic_regression
  from sklearn.utils import shuffle
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.validation import check_array
label_desc: Use image node_id 37 for calling a global function with example usage: test_isotonic_2darray_more_than_1_feature() without return types