code: def test_isotonic_ymin_ymax():
      # Test from @NelleV's issue:
      # https://github.com/scikit-learn/scikit-learn/issues/6921
      x = np.array(
          [
              1.263,
              1.318,
              -0.572,
              0.307,
              -0.707,
              -0.176,
              -1.599,
              1.059,
              1.396,
              1.906,
              0.210,
              0.028,
              -0.081,
              0.444,
              0.018,
              -0.377,
              -0.896,
              -0.377,
              -1.327,
              0.180,
          ]
      )
      y = isotonic_regression(x, y_min=0.0, y_max=0.1)
  
      assert np.all(y >= 0)
      assert np.all(y <= 0.1)
  
      # Also test decreasing case since the logic there is different
      y = isotonic_regression(x, y_min=0.0, y_max=0.1, increasing=False)
  
      assert np.all(y >= 0)
      assert np.all(y <= 0.1)
  
      # Finally, test with only one bound
      y = isotonic_regression(x, y_min=0.0, increasing=False)
  
      assert np.all(y >= 0)
  
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 25 for calling a global function with example usage: test_isotonic_ymin_ymax() without return types