code: def test_isotonic_make_unique_tolerance():
      # Check that averaging of targets for duplicate X is done correctly,
      # taking into account tolerance
      X = np.array([0, 1, 1 + 1e-16, 2], dtype=np.float64)
      y = np.array([0, 1, 2, 3], dtype=np.float64)
      ireg = IsotonicRegression().fit(X, y)
      y_pred = ireg.predict([0, 0.5, 1, 1.5, 2])
  
      assert_array_equal(y_pred, np.array([0, 0.75, 1.5, 2.25, 3]))
      assert_array_equal(ireg.X_thresholds_, np.array([0.0, 1.0, 2.0]))
      assert_array_equal(ireg.y_thresholds_, np.array([0.0, 1.5, 3.0]))
  
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 33 for calling a global function with example usage: test_isotonic_make_unique_tolerance() without return types