code: def test_isotonic_min_max_boundaries():
      # check if min value is used correctly
      ir = IsotonicRegression(y_min=2, y_max=4)
      n = 6
      x = np.arange(n)
      y = np.arange(n)
      y_test = [2, 2, 2, 3, 4, 4]
      y_result = np.round(ir.fit_transform(x, y))
      assert_array_equal(y_result, y_test)
  
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 18 for calling a global function with example usage: test_isotonic_min_max_boundaries() without return types