code: def test_make_unique_tolerance(dtype):
      # Check that equality takes account of np.finfo tolerance
      x = np.array([0, 1e-16, 1, 1 + 1e-14], dtype=dtype)
      y = x.copy()
      w = np.ones_like(x)
      x, y, w = _make_unique(x, y, w)
      if dtype == np.float64:
          x_out = np.array([0, 1, 1 + 1e-14])
      else:
          x_out = np.array([0, 1])
      assert_array_equal(x, x_out)
  
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 32 for calling a global function with example usage: test_make_unique_tolerance(dtype) without return types